diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index b322451..4fa8562 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -2,34 +2,34 @@ name: JUSTICE Unit Tests
 
 on:
   push:
-    branches:
-      - main
+    branches: [main]
   pull_request:
-    branches:
-      - main
+    branches: [main]
 
 jobs:
-  build:
+  test:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
         with:
-          lfs: true # Enable Git LFS support
+          lfs: true
 
-      - name: Set up Python 3.9
-        uses: actions/setup-python@v2
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.9
+          python-version: "3.10"
 
       - name: Install Poetry
         run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-          echo 'export PATH="$HOME/.local/bin:$PATH"' >> $GITHUB_ENV
+          python -m pip install --upgrade pip
+          python -m pip install "poetry==2.3.2"
+          poetry --version
 
       - name: Install dependencies
         run: |
-          poetry install
+          poetry config virtualenvs.in-project true
+          poetry install --no-interaction --no-ansi
 
       - name: Run Pytest
         env:
diff --git a/.gitignore b/.gitignore
index 50e5f05..b88ef7a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -232,8 +232,13 @@ limitarian_analysis.py
 paper_data_figures_generator.ipynb
 paper_data_figures_generator_codeocean.ipynb
 reproducing_data_and_plots.py
-figs/*
 ml_importance_plots/*
+figs/*
+solvers/moea/*.so
+solvers/moea/*.dylib
+solvers/moea/borgMOEA.py
+third_paper_visualizations.ipynb
+fourth_paper_analysis.ipynb
 # tests/verification_data/*
 #Mac
 .DS_Store
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..74c1554
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,28 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: JUSTICE Integrated Assessment Modelling Framework
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Palok
+    family-names: Biswas
+    email: p.biswas.nl@gmail.com
+    affiliation: Delft University of Technology
+    orcid: 'https://orcid.org/0000-0003-3830-7203'
+identifiers:
+  - type: doi
+    value: 10.5281/zenodo.15145121
+    description: Zenodo
+repository-code: 'https://github.com/pollockDeVis/JUSTICE'
+keywords:
+  - integrated assessment model
+  - climate-economy
+  - deep uncertainty
+  - normative uncertainty
+license: BSD-3-Clause
+version: 0.2.0
+date-released: '2025-06-23'
diff --git a/JUSTICE_example_run.ipynb b/JUSTICE_example_run.ipynb
index afc63ad..9119723 100644
--- a/JUSTICE_example_run.ipynb
+++ b/JUSTICE_example_run.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +171,9 @@
     "        # A subset of climate ensembles containing 100 ensemble members sampled using Latin Hypercube Sampling (LHS)\n",
     "        # climate_ensembles=[887, 899, 763, 4, 454, 728, 942, 543, 510, 913, 972, 384, 108, 899, 644, 607, 480, 260, 740, 177, 230, 531, 681, 468, 798, 835, 974, 401, 3, 517, 160, 151, 408, 274, 460, 541, 549, 377, 899, 278, 228, 972, 926, 116, 156, 189, 396, 414, 907, 284, 452, 510, 4, 636, 956, 859, 252, 785, 864, 299, 787, 894, 472, 254, 918, 924, 937, 95, 456, 599, 625, 485, 206, 694, 835, 376, 999, 30, 374, 729, 935, 816, 763, 136, 134, 114, 50, 533, 788, 745, 684, 510, 510, 763, 212, 364, 875, 731, 401, 476],\n",
     "        stochastic_run=False, # @OPTIONAL: This is to run the FaIR model in stochastic mode. Default is True \n",
-    "    )\n"
+    "        # clustering=True,  # @OPTIONAL: This is to enable clustering of climate ensembles. Default is False\n",
+    "        # cluster_level=5,\n",
+    "    )"
    ]
   },
   {
@@ -185,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,83 +275,6 @@
     "damage_cost_per_capita = datasets[\"damage_cost_per_capita\"]\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Representative indexes: [ 693  397  922  529  300  712  157  463  602  553  538  846  942  800\n",
-      "  130  591  294  756  486  252  462  765   94  958  450  163  965  623\n",
-      "  490  160  703  527  759  419  696  174   47  478  726  343  960  263\n",
-      "  155  818  823 1000  292  732  541  135]\n",
-      "Representative indexes (1-based): [ 694  398  923  530  301  713  158  464  603  554  539  847  943  801\n",
-      "  131  592  295  757  487  253  463  766   95  959  451  164  966  624\n",
-      "  491  161  704  528  760  420  697  175   48  479  727  344  961  264\n",
-      "  156  819  824 1001  293  733  542  136]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the temperature data\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.plot(time_horizon.model_time_horizon, temp, label='Global Temperature', color='blue')\n",
-    "plt.title('Global Temperature Over Time')\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Temperature Anomaly (°C)')\n",
-    "plt.show()\n",
-    "\n",
-    "summaries = temp[-1, :]\n",
-    "\n",
-    "N = 50  # number of members you want\n",
-    "quantile_indexes = np.argsort(summaries)\n",
-    "representative_indexes = []\n",
-    "\n",
-    "for i in range(N):\n",
-    "    start = int(i * (1001 / N))\n",
-    "    end = int((i+1) * (1001 / N))\n",
-    "    idx_in_quantile = np.random.choice(quantile_indexes[start:end])\n",
-    "    representative_indexes.append(idx_in_quantile)\n",
-    "\n",
-    "representative_indexes = np.array(representative_indexes)\n",
-    "print(\"Representative indexes:\", representative_indexes)\n",
-    "\n",
-    "# Print indexes but by adding 1 to each index for better readability\n",
-    "print(\"Representative indexes (1-based):\", representative_indexes + 1)\n",
-    "\n",
-    "# Now plot the subset of representative members\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "for idx in representative_indexes:\n",
-    "    plt.plot(time_horizon.model_time_horizon, temp[:, idx], label=f'Member {idx}', alpha=0.5)\n",
-    "plt.title('Representative Global Temperature Members')\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Temperature Anomaly (°C)')\n",
-    "plt.show()\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -571,6 +496,88 @@
     "    scenario_data[scenarios] = model.evaluate()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a subset of FaIR Ensemble Members "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Representative indexes: [229  19 810  57 745 508 679 776 502 667 351 547 566 800 130 574 301 659\n",
+      " 870 807 462 842 767 779 864 283 242 717 260 249 637 535 361   7 660 453\n",
+      " 858  75 499   6 192 505 867 945 970 760 454 342 220 124]\n",
+      "Representative indexes (1-based): [230  20 811  58 746 509 680 777 503 668 352 548 567 801 131 575 302 660\n",
+      " 871 808 463 843 768 780 865 284 243 718 261 250 638 536 362   8 661 454\n",
+      " 859  76 500   7 193 506 868 946 971 761 455 343 221 125]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the temperature data\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(time_horizon.model_time_horizon, temp, label='Global Temperature', color='blue')\n",
+    "plt.title('Global Temperature Over Time')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Temperature Anomaly (°C)')\n",
+    "plt.show()\n",
+    "\n",
+    "summaries = temp[-1, :]\n",
+    "\n",
+    "N = 50  # number of members you want\n",
+    "quantile_indexes = np.argsort(summaries)\n",
+    "representative_indexes = []\n",
+    "\n",
+    "for i in range(N):\n",
+    "    start = int(i * (1001 / N))\n",
+    "    end = int((i+1) * (1001 / N))\n",
+    "    idx_in_quantile = np.random.choice(quantile_indexes[start:end])\n",
+    "    representative_indexes.append(idx_in_quantile)\n",
+    "\n",
+    "representative_indexes = np.array(representative_indexes)\n",
+    "print(\"Representative indexes:\", representative_indexes)\n",
+    "\n",
+    "# Print indexes but by adding 1 to each index for better readability\n",
+    "print(\"Representative indexes (1-based):\", representative_indexes + 1)\n",
+    "\n",
+    "# Now plot the subset of representative members\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "for idx in representative_indexes:\n",
+    "    plt.plot(time_horizon.model_time_horizon, temp[:, idx], label=f'Member {idx}', alpha=0.5)\n",
+    "plt.title('Representative Global Temperature Members')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Temperature Anomaly (°C)')\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -601,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -613,7 +620,7 @@
     }
    ],
    "source": [
-    "from analysis.analyzer import run_optimization_adaptive\n",
+    "from run_optimization import run_optimization_adaptive\n",
     "import numpy as np\n",
     "import random\n",
     "from justice.util.enumerations import Optimizer\n",
diff --git a/README.md b/README.md
index 64c15d4..1602a2a 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,7 @@
 
 ![GitHub Actions build status](https://github.com/pollockDeVis/JUSTICE/actions/workflows/pytest.yml/badge.svg?event=push)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15145122.svg)](https://doi.org/10.5281/zenodo.15145122)
+
 <p align="center">
   <img title="JUSTICE Logo" alt="JUSTICE Logo" src="/docs/diagrams/JUSTICE LOGO.svg" width="400"/>
 </p>
@@ -12,7 +13,9 @@ JUSTICE is designed to explore the influence on distributive justice outcomes du
 
 ### JUSTICE Overview
 
-<img title="JUSTICE Framework" alt="Flowchart of JUSTICE" src="/docs/diagrams/JUSTICE Flowchart.jpeg">
+<p align="center">
+  <img title="JUSTICE Framework" alt="JUSTICE Framework" src="docs/diagrams/JUSTICE.svg" width="600"/>
+</p>
 
 ### Documentation
 
@@ -25,34 +28,23 @@ JUSTICE is developed by the [HIPPO Lab](https://www.tudelft.nl/ai/hippo-lab) at
   <img src="docs/diagrams/tudelft_logo_dark.png" alt="TU Delft Logo" width="200" style="margin-left: 20px;"/>
 </p>
 
+# Citation
 
-### Installation
-Create and activate a virtual environment and install as a package using
-
-```
-pip install .
-```
-
-### Running JUSTICE MOMARL
-
-#### Training
-
-JUSTICE-MOMARL can train multi-agent and multi-objective policies and currently support optimising for two objectives. JUSTICE-MOMARL converts the multi-objective training into a single objective by generating various policies with different weight combinations that weight each objective. The entire training process generates **100 different weight combinations** and trains each policy by linearising and normalising the reward and using **Multi Agent Proximal Policy Optimization (MAPPO)** as the reinforcement learning algorithm.
+To cite this code, please use the information in [CITATION.cff](CITATION.cff) and the following bibtex entry:
 
-Instead of training all the 100 weights together, we divide the training into batches and train a subset of weight combinations in each batch. For example, in order to train the first 10 uniform weight combnations, balancing the global economic output and inverse global temperature reward functions, run the following:
-
-```bash
-python train.py --start_uniform_weight 0 --end_uniform_weight 10 --env-config.rewards "global_economic_output" "inverse_global_temperature" --seed=<Seed> --base_save_path="Path where results are saved"
 ```
-
-You can view the additional argument to modify the JUSTICE modules and the RL hyperparameters by looking at the `Args` and `EnvConfig` dataclasses in `rl/args.py`.
-
-This script will save the checkpoints, pickled configs and multi-objective vectorised returns for trained policies for each weight combination in the base_save_path provided.
-
-#### Evaluation
-Once models have been trained for different weight combinations and the checkpoints for each of them are saved, you can run the evaluation for one specific weight combination and for one evaluation seed by running:
-
-```bash
-python eval.py --checkpoint_path="Absolute path to the checkpoint for policy" --seed=0 --output_path="Directory where evaluation results are saved""
+@inproceedings{ijcai2025p1064,
+title = {Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning},
+author = {Biswas, Palok and Osika, Zuzanna and Tamassia, Isidoro and Whorra, Adit and Zatarain-Salazar, Jazmin and Kwakkel, Jan and Oliehoek, Frans A. and Murukannaiah, Pradeep K.},
+booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
+Artificial Intelligence, {IJCAI-25}},
+publisher = {International Joint Conferences on Artificial Intelligence Organization},
+editor = {James Kwok},
+pages = {9573--9581},
+year = {2025},
+month = {8},
+note = {AI and Social Good},
+doi = {10.24963/ijcai.2025/1064},
+url = {https://doi.org/10.24963/ijcai.2025/1064},
+}
 ```
-The evaluation script will run the MOMARL policy in the JUSTICE simulation and save the evolution of various economic and climate metrics over time where the Emission Control Rate and Savings Rates are set by the trained policy in each time step.
\ No newline at end of file
diff --git a/analysis/analyzer.py b/analysis/analyzer.py
deleted file mode 100644
index 3a06561..0000000
--- a/analysis/analyzer.py
+++ /dev/null
@@ -1,590 +0,0 @@
-"""
-This module contains the uncertainty analysis for the JUSTICE model using EMA Workbench.
-"""
-
-import functools
-import datetime
-import numpy as np
-import os
-import random
-from justice.util.enumerations import *
-import json
-
-from solvers.moea.borgMOEA import BorgMOEA
-from ema_workbench.em_framework.optimization import EpsNSGAII
-
-# Suppress numpy version warnings
-import warnings
-
-warnings.filterwarnings("ignore")
-
-# EMA
-from ema_workbench import (
-    Model,
-    RealParameter,
-    ArrayOutcome,
-    ScalarOutcome,
-    CategoricalParameter,
-    ema_logging,
-    MultiprocessingEvaluator,
-    SequentialEvaluator,
-    MPIEvaluator,
-    Constant,
-    Scenario,
-)
-from ema_workbench.util.utilities import save_results, load_results
-from ema_workbench.em_framework.optimization import (
-    ArchiveLogger,
-    EpsilonProgress,
-    # HyperVolume,
-)
-
-# JUSTICE
-# Set this path to the src folder
-# export PYTHONPATH=$PYTHONPATH:/Users/palokbiswas/Desktop/pollockdevis_git/JUSTICE/src
-# from src.util.enumerations import Scenario
-from justice.util.EMA_model_wrapper import (
-    model_wrapper,
-    model_wrapper_emodps,
-    model_wrapper_static_optimization,
-)
-from justice.util.model_time import TimeHorizon
-from justice.util.data_loader import DataLoader
-
-from justice.util.enumerations import WelfareFunction, get_welfare_function_name
-from config.default_parameters import SocialWelfareDefaults
-
-SMALL_NUMBER = 1e-9  # Used to avoid division by zero in RBF calculations
-
-
-def run_optimization_adaptive(
-    config_path,
-    nfe=None,
-    # population_size=100,  # Default population size. For local machine, use smaller values like 5 or less
-    swf=0,
-    seed=None,
-    datapath="./data",
-    filename=None,
-    folder=None,
-    economy_type=Economy.NEOCLASSICAL,
-    damage_function_type=DamageFunction.KALKUHL,
-    abatement_type=Abatement.ENERDATA,
-    optimizer=Optimizer.EpsNSGAII,
-    evaluator=Evaluator.SequentialEvaluator,
-):
-
-    # Load configuration from file
-    with open(config_path, "r") as file:
-        config = json.load(file)
-
-    start_year = config["start_year"]
-    end_year = config["end_year"]
-    data_timestep = config["data_timestep"]
-    timestep = config["timestep"]
-    emission_control_start_year = config["emission_control_start_year"]
-    n_rbfs = config["n_rbfs"]
-    n_inputs = config["n_inputs"]
-    epsilons = config["epsilons"]
-    temperature_year_of_interest = config["temperature_year_of_interest"]
-    reference_ssp_rcp_scenario_index = config["reference_ssp_rcp_scenario_index"]
-
-    stochastic_run = config["stochastic_run"]
-
-    # Check if climate_ensemble_members is in the config, if not set it to None
-    if "climate_ensemble_members" in config:
-        climate_ensemble_members = config["climate_ensemble_members"]
-    else:
-        climate_ensemble_members = None
-
-    social_welfare_function = WelfareFunction.from_index(swf)
-    social_welfare_function_type = social_welfare_function.value[
-        0
-    ]  # Gets the first value of the tuple with index 0
-
-    model = Model("JUSTICE", function=model_wrapper_emodps)
-
-    # Instantiate classes and compute derived parameters
-    data_loader = DataLoader()
-    time_horizon = TimeHorizon(
-        start_year=start_year,
-        end_year=end_year,
-        data_timestep=data_timestep,
-        timestep=timestep,
-    )
-    emission_control_start_timestep = time_horizon.year_to_timestep(
-        year=emission_control_start_year, timestep=timestep
-    )
-    temperature_year_of_interest_index = time_horizon.year_to_timestep(
-        year=temperature_year_of_interest, timestep=timestep
-    )
-
-    # Define constants, uncertainties and levers
-    model.constants = [
-        Constant("n_regions", len(data_loader.REGION_LIST)),
-        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
-        Constant("emission_control_start_timestep", emission_control_start_timestep),
-        Constant("n_rbfs", n_rbfs),
-        Constant("n_inputs_rbf", n_inputs),
-        Constant("n_outputs_rbf", len(data_loader.REGION_LIST)),
-        Constant("social_welfare_function_type", social_welfare_function_type),
-        Constant("economy_type", economy_type.value),
-        Constant("damage_function_type", damage_function_type.value),
-        Constant("abatement_type", abatement_type.value),
-        Constant(
-            "temperature_year_of_interest_index", temperature_year_of_interest_index
-        ),
-        Constant("stochastic_run", stochastic_run),
-        Constant("climate_ensemble_members", climate_ensemble_members),
-    ]
-
-    # Speicify uncertainties
-    model.uncertainties = [
-        CategoricalParameter(
-            "ssp_rcp_scenario",
-            (
-                0,
-                1,
-                2,
-                3,
-                4,
-                5,
-                6,
-                7,
-            ),  # TODO should have a configuration file for optimizations
-        ),  # 8 SSP-RCP scenario combinations
-    ]
-
-    # Set the model levers, which are the RBF parameters
-    # These are the formula to calculate the number of centers, radii and weights
-
-    centers_shape = (
-        n_rbfs * n_inputs
-    )  # centers = n_rbfs x n_inputs # radii = n_rbfs x n_inputs
-    weights_shape = (
-        len(data_loader.REGION_LIST) * n_rbfs
-    )  # weights = n_outputs x n_rbfs
-
-    centers_levers = []
-    radii_levers = []
-    weights_levers = []
-
-    for i in range(centers_shape):
-        centers_levers.append(
-            RealParameter(f"center {i}", -1.0, 1.0)
-        )  # TODO should have a configuration file for optimizations
-        radii_levers.append(
-            RealParameter(f"radii {i}", SMALL_NUMBER, 1.0)
-        )  # Changed from 0 to SMALL_NUMBER to avoid division by zero in RBF calculations
-
-    for i in range(weights_shape):
-        weights_levers.append(
-            RealParameter(f"weights {i}", SMALL_NUMBER, 1.0)
-        )  # Probably this range determines the min and max values of the ECR
-
-    # Set the model levers
-    model.levers = centers_levers + radii_levers + weights_levers
-
-    model.outcomes = [
-        ScalarOutcome(
-            "welfare",
-            variable_name="welfare",
-            kind=ScalarOutcome.MINIMIZE,
-        ),
-        # ScalarOutcome(
-        #     "years_above_temperature_threshold",
-        #     variable_name="years_above_threshold",
-        #     kind=ScalarOutcome.MINIMIZE,
-        # ),
-        ScalarOutcome(
-            "fraction_above_threshold",
-            variable_name="fraction_above_threshold",
-            kind=ScalarOutcome.MINIMIZE,
-        ),
-        # NOTE : Temporarily commented out for bi-objective optimization
-        # ScalarOutcome(
-        #     "welfare_loss_damage",
-        #     variable_name="welfare_loss_damage",
-        #     kind=ScalarOutcome.MAXIMIZE,
-        # ),
-        # ScalarOutcome(
-        #     "welfare_loss_abatement",
-        #     variable_name="welfare_loss_abatement",
-        #     kind=ScalarOutcome.MAXIMIZE,
-        # ),
-    ]
-
-    reference_scenario = Scenario(
-        "reference",
-        ssp_rcp_scenario=reference_ssp_rcp_scenario_index,
-    )
-
-    # Add social_welfare_function.value[1] to the filename
-    filename = f"{social_welfare_function.value[1]}_{nfe}_{seed}.tar.gz"
-    date = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
-
-    directory_name = os.path.join(
-        datapath, f"{social_welfare_function.value[1]}_{date}_{seed}"
-    )
-    # Create a directory inside ./data/ with name output_{date} to save the results
-    os.makedirs(directory_name, exist_ok=True)
-    # Set the directory path to a variable
-
-    convergence_metrics = [
-        ArchiveLogger(
-            directory_name,
-            [l.name for l in model.levers],
-            [o.name for o in model.outcomes],
-            base_filename=filename,
-        ),
-        EpsilonProgress(),
-    ]
-    algorithm = None
-    if optimizer == Optimizer.EpsNSGAII:
-        algorithm = EpsNSGAII
-    elif optimizer == Optimizer.BorgMOEA:
-        algorithm = BorgMOEA
-
-    if evaluator == Evaluator.MPIEvaluator:
-        with MPIEvaluator(model) as evaluator:  # Use this for HPC
-            results = evaluator.optimize(
-                searchover="levers",
-                nfe=nfe,
-                epsilons=epsilons,
-                reference=reference_scenario,
-                convergence=convergence_metrics,
-                # population_size=population_size,
-                algorithm=algorithm,
-            )
-    elif evaluator == Evaluator.MultiprocessingEvaluator:
-        with MultiprocessingEvaluator(model) as evaluator:
-            results = evaluator.optimize(
-                searchover="levers",
-                nfe=nfe,
-                epsilons=epsilons,
-                reference=reference_scenario,
-                convergence=convergence_metrics,
-                # population_size=population_size,
-                algorithm=algorithm,
-            )
-    else:
-
-        # with MPIEvaluator(model) as evaluator:  # Use this for HPC
-        with SequentialEvaluator(model) as evaluator:  # Use this for local machine
-            results = evaluator.optimize(
-                searchover="levers",
-                nfe=nfe,
-                epsilons=epsilons,
-                reference=reference_scenario,
-                convergence=convergence_metrics,
-                # population_size=population_size,  # NOTE set population parameters for local machine. It is faster for testing
-                algorithm=algorithm,
-            )
-
-
-#######################################################################################################################################################
-# Deprecated
-#######################################################################################################################################################
-
-
-def run_optimization_static(nfe=5000, filename=None, folder=None):
-
-    # TODO: Update this model wrapper. [Deprecated]
-
-    model = Model("JUSTICE", function=model_wrapper_static_optimization)
-
-    # Define constants, uncertainties and levers
-    model.constants = [
-        Constant("n_regions", len(data_loader.REGION_LIST)),
-        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
-        Constant("elasticity_of_marginal_utility_of_consumption", 1.45),
-        Constant("pure_rate_of_social_time_preference", 0.015),
-    ]
-
-    # Speicify uncertainties
-    model.uncertainties = [
-        CategoricalParameter(
-            "ssp_rcp_scenario", (0, 1, 2, 3, 4, 5, 6, 7)
-        ),  # 8 SSP-RCP scenario combinations
-        CategoricalParameter("inequality_aversion", (0.0, 0.5, 1.45, 2.0)),
-        # Add Discount rate as a RealParameter Uncertainty
-        # RealParameter("pure_rate_of_social_time_preference", 0.0001, 0.020),
-    ]
-
-    # Set the model levers, which are the RBF parameters
-
-    ecr_levers = []
-    for i in range(len(data_loader.REGION_LIST)):
-        for j in range(len(time_horizon.model_time_horizon)):
-            ecr_levers.append(
-                RealParameter(f"emissions_control_rate {i} {j}", 0.00, 1.0)
-            )
-
-    # Set the model levers
-    model.levers = ecr_levers
-
-    model.outcomes = [
-        ScalarOutcome(
-            "mean_welfare_utilitarian",
-            variable_name="welfare_utilitarian",
-            kind=ScalarOutcome.MAXIMIZE,
-        ),
-    ]
-
-    reference_scenario = Scenario(
-        "reference",
-        ssp_rcp_scenario=2,
-        inequality_aversion=0.0,
-    )
-
-    convergence_metrics = [
-        ArchiveLogger(
-            "./data/output",
-            [l.name for l in model.levers],
-            [o.name for o in model.outcomes],
-            base_filename="JUSTICE_dps_archive.tar.gz",
-        ),
-        EpsilonProgress(),
-    ]
-
-    with SequentialEvaluator(model) as evaluator:
-        results = evaluator.optimize(
-            searchover="levers",
-            nfe=nfe,
-            epsilons=[0.01] * len(model.outcomes),  # * len(model.outcomes)
-            reference=reference_scenario,
-            convergence=convergence_metrics,
-        )
-
-
-#######################################################################################################################################################
-
-
-def perform_exploratory_analysis(number_of_experiments=10, filename=None, folder=None):
-    # TODO: Update this model wrapper. [Deprecated]
-    # Instantiate the model
-    model = Model("JUSTICE", function=model_wrapper)
-    model.constants = [
-        Constant("n_regions", len(data_loader.REGION_LIST)),
-        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
-    ]
-
-    # Speicify uncertainties
-    model.uncertainties = [
-        CategoricalParameter(
-            "ssp_rcp_scenario", (0, 1, 2, 3, 4, 5, 6, 7)
-        ),  # 8 SSP-RCP scenario combinations
-        RealParameter("elasticity_of_marginal_utility_of_consumption", 0.0, 2.0),
-        RealParameter(
-            "pure_rate_of_social_time_preference", 0.0001, 0.020
-        ),  # 0.1 to 3% in RICE50 gazzotti2
-        RealParameter("inequality_aversion", 0.0, 2.0),  # 0.2 -2.5
-    ]
-
-    # Set model levers - has to be 2D array of shape (57, 286) 57 regions and 286 timesteps
-
-    # TODO temporarily commented out
-    # sr_levers = []
-    # ecr_levers = []
-    # for i in range(len(data_loader.REGION_LIST)):
-    #     for j in range(len(time_horizon.model_time_horizon)):
-    #         sr_levers.append(RealParameter(f"savings_rate {i} {j}", 0.05, 0.5))
-    #         ecr_levers.append(
-    #             RealParameter(f"emissions_control_rate {i} {j}", 0.00, 1.0)
-    #         )
-
-    # model.levers = sr_levers + ecr_levers
-
-    # Specify outcomes #All outcomes have shape (57, 286, 1001) except global_temperature which has shape (286, 1001)
-    model.outcomes = [
-        ArrayOutcome(
-            "mean_net_economic_output",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="net_economic_output",
-        ),
-        ArrayOutcome(
-            "5p_net_economic_output",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="net_economic_output",
-        ),
-        ArrayOutcome(
-            "95p_net_economic_output",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="net_economic_output",
-        ),
-        ArrayOutcome(
-            "mean_consumption_per_capita",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="consumption_per_capita",
-        ),
-        ArrayOutcome(
-            "5p_consumption_per_capita",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="consumption_per_capita",
-        ),
-        ArrayOutcome(
-            "95p_consumption_per_capita",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="consumption_per_capita",
-        ),
-        ArrayOutcome(
-            "mean_emissions",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="emissions",
-        ),
-        ArrayOutcome(
-            "5p_emissions",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="emissions",
-        ),
-        ArrayOutcome(
-            "95p_emissions",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="emissions",
-        ),
-        ArrayOutcome(
-            "mean_economic_damage",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="economic_damage",
-        ),
-        ArrayOutcome(
-            "5p_economic_damage",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="economic_damage",
-        ),
-        ArrayOutcome(
-            "95p_economic_damage",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="economic_damage",
-        ),
-        ArrayOutcome(
-            "mean_abatement_cost",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="abatement_cost",
-        ),
-        ArrayOutcome(
-            "5p_abatement_cost",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="abatement_cost",
-        ),
-        ArrayOutcome(
-            "95p_abatement_cost",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="abatement_cost",
-        ),
-        ArrayOutcome(
-            "mean_disentangled_utility",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="disentangled_utility",
-        ),
-        ArrayOutcome(
-            "5p_disentangled_utility",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="disentangled_utility",
-        ),
-        ArrayOutcome(
-            "95p_disentangled_utility",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="disentangled_utility",
-        ),
-        ArrayOutcome(
-            "mean_consumption",
-            function=functools.partial(np.mean, axis=2),
-            variable_name="consumption",
-        ),
-        ArrayOutcome(
-            "5p_consumption",
-            function=functools.partial(np.percentile, q=5, axis=2),
-            variable_name="consumption",
-        ),
-        ArrayOutcome(
-            "95p_consumption",
-            function=functools.partial(np.percentile, q=95, axis=2),
-            variable_name="consumption",
-        ),
-        # ArrayOutcome("consumption", function=functools.partial(np.mean, axis=2)),
-        # ArrayOutcome("welfare_utilitarian"),  # (286, 1001) #, function=get_mean_2D
-        # ArrayOutcome("consumption_per_capita", function=apply_statistical_functions),
-        # ArrayOutcome("emissions", function=apply_statistical_functions),
-        ArrayOutcome(
-            "mean_global_temperature",
-            function=functools.partial(np.mean, axis=1),
-            variable_name="global_temperature",
-        ),  # (286, 1001)
-        ArrayOutcome(
-            "5p_global_temperature",
-            function=functools.partial(np.percentile, q=5, axis=1),
-            variable_name="global_temperature",
-        ),  # (286, 1001)
-        ArrayOutcome(
-            "95p_global_temperature",
-            function=functools.partial(np.percentile, q=95, axis=1),
-            variable_name="global_temperature",
-        ),
-        ArrayOutcome(
-            "mean_welfare_utilitarian",
-            function=functools.partial(np.mean, axis=1),
-            variable_name="welfare_utilitarian",
-        ),  # (286, 1001)
-        ArrayOutcome(
-            "5p_welfare_utilitarian",
-            function=functools.partial(np.percentile, q=5, axis=1),
-            variable_name="welfare_utilitarian",
-        ),  # (286, 1001)
-        ArrayOutcome(
-            "95p_welfare_utilitarian",
-            function=functools.partial(np.percentile, q=95, axis=1),
-            variable_name="welfare_utilitarian",
-        ),  # (286, 1001)
-        # (286, 1001)
-        # ArrayOutcome("economic_damage", function=apply_statistical_functions),
-        # ArrayOutcome("abatement_cost", function=apply_statistical_functions),
-        # ArrayOutcome("disentangled_utility", function=apply_statistical_functions),
-    ]
-
-    with MultiprocessingEvaluator(model) as evaluator:
-        results = evaluator.perform_experiments(
-            scenarios=number_of_experiments,
-            reporting_frequency=100,  # policies=2,TODO temporarily commented out
-        )
-
-        if filename is None:
-            file_name = f"optimal_open_exploration_{number_of_experiments}.tar.gz"
-
-        if folder is None:
-            target_directory = os.path.join(os.getcwd(), "data/output", file_name)
-        else:
-            target_directory = os.path.join(folder, file_name)
-
-        save_results(results, file_name=target_directory)
-
-
-if __name__ == "__main__":
-    seeds = [
-        9845531,
-        1644652,
-        3569126,
-        6075612,
-        521475,
-    ]
-
-    config_path = "analysis/normative_uncertainty_optimization.json"  # This loads the config used in the Paper
-
-    ema_logging.log_to_stderr(ema_logging.INFO)
-
-    seed = seeds[4]
-    random.seed(seed)
-    np.random.seed(seed)
-    # perform_exploratory_analysis(number_of_experiments=10, filename=None, folder=None)
-    # run_optimization_adaptive(
-    #     n_rbfs=4, n_inputs=2, nfe=5, swf=4, filename=None, folder=None, seed=seed
-    # )
-    run_optimization_adaptive(
-        config_path=config_path,
-        nfe=5,
-        swf=0,
-        seed=seed,
-        datapath="./data",
-        optimizer=Optimizer.EpsNSGAII,
-        population_size=2,  # Optimizer.BorgMOEA,
-        evaluator=Evaluator.SequentialEvaluator,
-    )
diff --git a/analysis/hpc_run.py b/analysis/hpc_run.py
index 5804958..d86a8ce 100644
--- a/analysis/hpc_run.py
+++ b/analysis/hpc_run.py
@@ -1,25 +1,139 @@
-from analysis.analyzer import run_optimization_adaptive
+from run_optimization import run_optimization_momadps, run_single_agent_momadps
 import sys
 import random
 import numpy as np
 from justice.util.enumerations import Optimizer, Evaluator
 
-config_path = "analysis/normative_uncertainty_optimization.json"  # This loads the config used in the Paper
+CONFIG_PATH = "analysis/momadps_config.json"
+NASH_PROFILES_PATH = "pareto_nash_profiles.csv"
+POLICY_BANK_PATH = "COMBINED_MOMA_epsilon_nondominated_set.csv"
 
 if __name__ == "__main__":
-    nfe = int(sys.argv[1]) if len(sys.argv) > 1 else 5  # default value 5
-    swf = int(sys.argv[2]) if len(sys.argv) > 2 else 0  # default value 0
-    seed = int(sys.argv[3]) if len(sys.argv) > 3 else 5000  # default value 5000
+    nfe = int(sys.argv[1]) if len(sys.argv) > 1 else 5
+    swf = int(sys.argv[2]) if len(sys.argv) > 2 else 0  # unused, keep for compatibility
+    seed = int(sys.argv[3]) if len(sys.argv) > 3 else 5000
+    scenario_index = int(sys.argv[4]) if len(sys.argv) > 4 else 2
+    policy_index = int(sys.argv[5]) if len(sys.argv) > 5 else 0
+    variable_macro_index = int(sys.argv[6]) if len(sys.argv) > 6 else 0
+    population_size = int(sys.argv[7]) if len(sys.argv) > 7 else 100
 
-    # Setting the seed
     random.seed(seed)
     np.random.seed(seed)
-    run_optimization_adaptive(
-        config_path=config_path,
+
+    run_single_agent_momadps(
+        config_path=CONFIG_PATH,
+        nash_profiles_path=NASH_PROFILES_PATH,
+        policy_bank_path=POLICY_BANK_PATH,
+        policy_index=policy_index,
+        variable_macro_index=variable_macro_index,
         nfe=nfe,
-        swf=swf,
         seed=seed,
         datapath="./data",
-        optimizer=Optimizer.BorgMOEA,  # Optimizer.BorgMOEA, Optimizer.EpsNSGAII
+        optimizer=Optimizer.MMBorgMOEA,
+        population_size=population_size,
+        reference_ssp_rcp_scenario_index=scenario_index,
         evaluator=Evaluator.SequentialEvaluator,
+        epsilons=[1e-3, 1e-3],
     )
+
+
+################
+
+# from run_optimization import run_optimization_adaptive
+# import sys
+# import random
+# import numpy as np
+# from justice.util.enumerations import Optimizer, Evaluator
+
+# config_path = "analysis/normative_uncertainty_optimization.json"  # This loads the config used in the Paper
+
+# if __name__ == "__main__":
+#     nfe = int(sys.argv[1]) if len(sys.argv) > 1 else 5  # default value 5
+#     swf = int(sys.argv[2]) if len(sys.argv) > 2 else 0  # default value 0
+#     seed = int(sys.argv[3]) if len(sys.argv) > 3 else 5000  # default value 5000
+#     scenario_index = int(sys.argv[4]) if len(sys.argv) > 4 else 2  # default value 2
+
+#     # Setting the seed
+#     random.seed(seed)
+#     np.random.seed(seed)
+#     run_optimization_adaptive(
+#         config_path=config_path,
+#         nfe=nfe,
+#         swf=swf,
+#         seed=seed,
+#         datapath="./data",
+#         optimizer=Optimizer.MMBorgMOEA,  # Optimizer.BorgMOEA, Optimizer.EpsNSGAII
+#         population_size=100,
+#         reference_ssp_rcp_scenario_index=scenario_index,
+#         evaluator=Evaluator.SequentialEvaluator,
+#     )
+
+
+######################
+
+# from analyzer import run_optimization_momadps
+# import sys
+# import random
+# import numpy as np
+# from justice.util.enumerations import Optimizer, Evaluator
+
+# config_path = "analysis/momadps_config.json"  # This loads the config used in the Paper
+
+# if __name__ == "__main__":
+#     nfe = int(sys.argv[1]) if len(sys.argv) > 1 else 5  # default value 5
+#     swf = int(sys.argv[2]) if len(sys.argv) > 2 else 0  # default value 0
+#     seed = int(sys.argv[3]) if len(sys.argv) > 3 else 5000  # default value 5000
+#     scenario_index = int(sys.argv[4]) if len(sys.argv) > 4 else 2  # default value 2
+
+#     # Setting the seed
+#     random.seed(seed)
+#     np.random.seed(seed)
+#     run_optimization_momadps(
+#         config_path=config_path,
+#         nfe=nfe,
+#         seed=seed,
+#         datapath="./data",
+#         optimizer=Optimizer.MMBorgMOEA,  # Optimizer.BorgMOEA, Optimizer.EpsNSGAII
+#         population_size=100,
+#         reference_ssp_rcp_scenario_index=scenario_index,
+#         evaluator=Evaluator.SequentialEvaluator,
+#     )
+
+#######################################################
+
+# from analyzer import run_optimization_momadps, run_single_agent_momadps
+# import sys
+# import random
+# import numpy as np
+# from justice.util.enumerations import Optimizer, Evaluator
+
+# CONFIG_PATH = "analysis/momadps_config.json"
+# REFERENCE_SET_PATH = "data/temporary/MOMA_DATA/200k/MOMA_reference_set.csv"
+
+# if __name__ == "__main__":
+#     nfe = int(sys.argv[1]) if len(sys.argv) > 1 else 5
+#     swf = int(sys.argv[2]) if len(sys.argv) > 2 else 0  # unused, keep for compatibility
+#     seed = int(sys.argv[3]) if len(sys.argv) > 3 else 5000
+#     scenario_index = int(sys.argv[4]) if len(sys.argv) > 4 else 2
+#     policy_index = int(sys.argv[5]) if len(sys.argv) > 5 else 0
+#     variable_macro_index = int(sys.argv[6]) if len(sys.argv) > 6 else 0
+#     population_size = int(sys.argv[7]) if len(sys.argv) > 7 else 100
+
+#     random.seed(seed)
+#     np.random.seed(seed)
+
+#     run_single_agent_momadps(
+#         config_path=CONFIG_PATH,
+#         reference_set_path=REFERENCE_SET_PATH,
+#         policy_index=policy_index,
+#         variable_macro_index=variable_macro_index,
+#         nfe=nfe,
+#         seed=seed,
+#         datapath="./data",
+#         optimizer=Optimizer.MMBorgMOEA,
+#         population_size=population_size,
+#         reference_ssp_rcp_scenario_index=scenario_index,
+#         evaluator=Evaluator.SequentialEvaluator,
+#         epsilons=[1e-3, 1e-3],
+#     )
+#######################################################
diff --git a/analysis/hpc_slurm_scripts/mm_borg_exclusive.sh b/analysis/hpc_slurm_scripts/mm_borg_exclusive.sh
new file mode 100644
index 0000000..c6bc780
--- /dev/null
+++ b/analysis/hpc_slurm_scripts/mm_borg_exclusive.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+#SBATCH --job-name=ExBorg
+#SBATCH --partition=compute-p2
+#SBATCH --time=00:10:00
+#SBATCH --nodes=1
+#SBATCH --ntasks=61          # 5 islands, 11 workers each -> 61 ranks
+#SBATCH --cpus-per-task=1
+#SBATCH --exclusive
+#SBATCH --mem=0
+#SBATCH --account=research-tpm-mas
+#SBATCH --output=logs/%x-%j.out
+#SBATCH --error=logs/%x-%j.err
+
+module load 2025
+module load openmpi
+module load miniconda3
+source "$(conda info --base)/etc/profile.d/conda.sh"
+conda activate justice311
+
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+cd "$SLURM_SUBMIT_DIR"
+mkdir -p logs
+
+export BORG_ISLANDS=5
+
+nfe=2000
+myswf=0
+seed=100
+scenario_index=2
+
+mpirun -np "$SLURM_NTASKS" python hpc_run.py "$nfe" "$myswf" "$seed" "$scenario_index"
\ No newline at end of file
diff --git a/analysis/hpc_slurm_scripts/mm_justice.sh b/analysis/hpc_slurm_scripts/mm_justice.sh
new file mode 100644
index 0000000..462a157
--- /dev/null
+++ b/analysis/hpc_slurm_scripts/mm_justice.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+#SBATCH --job-name="200kMM"
+#SBATCH --partition=memory
+#SBATCH --time=30:00:00
+#SBATCH --ntasks=45
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=16G
+#SBATCH --account=research-tpm-mas
+#SBATCH --output=logs/%x-%j.out
+#SBATCH --error=logs/%x-%j.err    # <- add an explicit error log
+
+module load 2025
+module load openmpi
+
+
+source /scratch/$USER/.conda/etc/profile.d/conda.sh
+conda activate justice311
+
+# Prevent threaded BLAS from oversubscribing
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+cd "$SLURM_SUBMIT_DIR"
+mkdir -p logs                 # <- ensure logs dir exists
+
+# MS: 1 master + 1 worker (BORG_ISLANDS env not used by MSBorgMOEA, but harmless) N islands each with K workers, request N*(K+1) + 1 MPI nodes when submitting the job
+export BORG_ISLANDS=4
+
+# Per-run args
+nfe=200000
+myswf=0
+seed=555
+scenario_index=2
+
+mpirun -np "$SLURM_NTASKS" python hpc_run.py "$nfe" "$myswf" "$seed" "$scenario_index"
\ No newline at end of file
diff --git a/analysis/hpc_slurm_scripts/run_single_agent_array.sh b/analysis/hpc_slurm_scripts/run_single_agent_array.sh
new file mode 100644
index 0000000..a8b180e
--- /dev/null
+++ b/analysis/hpc_slurm_scripts/run_single_agent_array.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+#SBATCH --job-name="SingleAgent_MOMA"
+#SBATCH --partition=memory
+#SBATCH --time=00:10:00
+#SBATCH --ntasks=45
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=16G
+#SBATCH --account=research-tpm-mas
+#SBATCH --output=logs/%x-%j.out
+#SBATCH --error=logs/%x-%j.err
+#SBATCH --array=0-4    # one task per macro agent (0..4)
+
+module load 2025
+module load openmpi
+
+source /scratch/$USER/.conda/etc/profile.d/conda.sh
+conda activate justice311
+
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+
+cd "$SLURM_SUBMIT_DIR"
+mkdir -p logs
+
+export BORG_ISLANDS=4
+
+# Base run parameters
+nfe=200
+myswf=0
+seed=4444
+scenario_index=2
+policy_index=0    # <-- row index in pareto_nash_profiles.csv (was MOMA_reference_set.csv)
+pop_size=100
+
+# Each array task gets its macro index from SLURM_ARRAY_TASK_ID
+macro_index=${SLURM_ARRAY_TASK_ID}
+
+echo "Starting single-agent Nash verification run:"
+echo "  nfe               = $nfe"
+echo "  seed              = $seed"
+echo "  scenario_index    = $scenario_index"
+echo "  policy_index      = $policy_index  (row in pareto_nash_profiles.csv)"
+echo "  variable_macro_idx= $macro_index"
+echo "  population_size   = $pop_size"
+echo "  MPI tasks         = $SLURM_NTASKS"
+
+mpirun -np "$SLURM_NTASKS" python hpc_run.py \
+    "$nfe" "$myswf" "$seed" "$scenario_index" \
+    "$policy_index" "$macro_index" "$pop_size"
diff --git a/analysis/momadps_config.json b/analysis/momadps_config.json
new file mode 100644
index 0000000..d243fd6
--- /dev/null
+++ b/analysis/momadps_config.json
@@ -0,0 +1,78 @@
+{
+    "start_year": 2015,
+    "end_year": 2300,
+    "data_timestep": 5,
+    "timestep": 1,
+    "emission_control_start_year": 2025,
+    "n_inputs": 3,
+    "epsilons": [
+        10,
+        10,
+        10,
+        10,
+        10,
+        0.01
+    ],
+
+    "min_temperature": 0.5,
+    "max_temperature": 5.0,
+    "min_temperature_change": -0.5,
+    "max_temperature_change": 2.0,
+    "consumption_min": 20.0,
+    "consumption_max": 3500.0,
+
+    "climate_ensemble_members" : [
+                694,
+                398,
+                923,
+                530,
+                301,
+                713,
+                158,
+                464,
+                603,
+                554,
+                539,
+                847,
+                943,
+                801,
+                131,
+                592,
+                295,
+                757,
+                487,
+                253,
+                463,
+                766,
+                95,
+                959,
+                451,
+                164,
+                966,
+                624,
+                491,
+                161,
+                704,
+                528,
+                760,
+                420,
+                697,
+                175,
+                48,
+                479,
+                727,
+                344,
+                961,
+                264,
+                156,
+                819,
+                824,
+                1001,
+                293,
+                733,
+                542,
+                136
+            ],
+    "stochastic_run": 0,
+    "temperature_year_of_interest": 2100
+}
\ No newline at end of file
diff --git a/analysis/normative_uncertainty_optimization.json b/analysis/normative_uncertainty_optimization.json
index b1f58dd..c90a45a 100644
--- a/analysis/normative_uncertainty_optimization.json
+++ b/analysis/normative_uncertainty_optimization.json
@@ -6,11 +6,11 @@
     "emission_control_start_year": 2025,
     "n_rbfs": 4,
     "n_inputs": 2,
-    "reference_ssp_rcp_scenario_index": 2,
     "epsilons": [
-        0.0001,
-        0.01
+        0.00001,
+        0.001
     ],
+    "reference_ssp_rcp_scenario_index": 2,
     "climate_ensemble_members" : [
                 694,
                 398,
diff --git a/data/input/region_to_macro_5.npy b/data/input/region_to_macro_5.npy
new file mode 100644
index 0000000..ce3b31c
Binary files /dev/null and b/data/input/region_to_macro_5.npy differ
diff --git a/docs/diagrams/JUSTICE.svg b/docs/diagrams/JUSTICE.svg
new file mode 100644
index 0000000..89968f4
--- /dev/null
+++ b/docs/diagrams/JUSTICE.svg
@@ -0,0 +1,167 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg id="Layer_2" data-name="Layer 2" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 801.714 548.082">
+  <defs>
+    <style>
+      .cls-1 {
+        stroke-dasharray: 8.572;
+        stroke-linejoin: round;
+      }
+
+      .cls-1, .cls-2, .cls-3 {
+        stroke-width: 1.714px;
+      }
+
+      .cls-1, .cls-2, .cls-3, .cls-4 {
+        fill: none;
+        stroke: #000;
+      }
+
+      .cls-5 {
+        font-family: OptimaNovaLTPro-Black, 'Optima nova LT Pro';
+        font-size: 19px;
+        font-weight: 800;
+      }
+
+      .cls-5, .cls-6 {
+        fill: #231f20;
+      }
+
+      .cls-2 {
+        stroke-miterlimit: 5;
+      }
+
+      .cls-3, .cls-4 {
+        stroke-miterlimit: 10;
+      }
+
+      .cls-7 {
+        letter-spacing: 0em;
+      }
+
+      .cls-8, .cls-6 {
+        font-family: Menlo-Regular, Menlo;
+        font-size: 18.859px;
+      }
+
+      .cls-4 {
+        stroke-width: 3.429px;
+      }
+
+      .cls-9 {
+        letter-spacing: -.036em;
+      }
+
+      .cls-10 {
+        letter-spacing: -.03em;
+      }
+
+      .cls-11 {
+        letter-spacing: -.009em;
+      }
+
+      .cls-12 {
+        letter-spacing: .07em;
+      }
+    </style>
+  </defs>
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+    </g>
+    <g>
+      <line class="cls-4" x1="134.791" y1="322.825" x2="134.791" y2="417.065"/>
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+    </g>
+    <g>
+      <path class="cls-4" d="M402.041,242.173V112.297c0-9.43-7.715-17.145-17.145-17.145h-149.255"/>
+      <polygon points="238.143 86.603 223.338 95.152 238.143 103.701 238.143 86.603"/>
+    </g>
+    <g>
+      <path class="cls-4" d="M402.041,322.825v129.877c0,9.43-7.715,17.14-17.145,17.135l-149.255-.085"/>
+      <polygon points="238.148 461.205 223.338 469.745 238.138 478.302 238.148 461.205"/>
+    </g>
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+      <line class="cls-4" x1="490.589" y1="282.449" x2="514.241" y2="282.449"/>
+      <polygon points="511.74 290.998 526.544 282.449 511.74 273.9 511.74 290.998"/>
+    </g>
+    <text class="cls-5" transform="translate(8.724 22.553)"><tspan x="0" y="0">Damage</tspan></text>
+    <text class="cls-5" transform="translate(8.724 209.998)"><tspan x="0" y="0">Economy</tspan></text>
+    <text class="cls-5" transform="translate(8.847 396.337)"><tspan x="0" y="0">Abatement</tspan></text>
+    <text class="cls-5" transform="translate(541.009 22.553)"><tspan x="0" y="0">Social </tspan><tspan class="cls-9" x="56.752" y="0">W</tspan><tspan x="75.354" y="0">elfa</tspan><tspan class="cls-11" x="107.614" y="0">r</tspan><tspan x="115.139" y="0">e </tspan><tspan class="cls-10" x="129.236" y="0">F</tspan><tspan class="cls-7" x="138.053" y="0">unc</tspan><tspan class="cls-12" x="170.713" y="0">t</tspan><tspan x="178.636" y="0">ion</tspan></text>
+    <text class="cls-5" transform="translate(298.961 209.998)"><tspan x="0" y="0">Climate</tspan></text>
+    <text class="cls-6" transform="translate(94.747 103.674)"><tspan x="0" y="0">Kalkuhl</tspan></text>
+    <text class="cls-6" transform="translate(94.747 289.299)"><tspan x="0" y="0">RICE50+</tspan></text>
+    <text class="cls-6" transform="translate(85.284 473.73)"><tspan x="0" y="0">EnerData</tspan></text>
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+    <text class="cls-8" transform="translate(601.849 111.537)"><tspan x="0" y="0">Utilitarian</tspan></text>
+    <text class="cls-8" transform="translate(605.142 225.028)"><tspan x="0" y="0">Prioritarian</tspan></text>
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+    <text class="cls-8" transform="translate(603.207 469.746)"><tspan x="0" y="0">Egalitarian</tspan></text>
+    <rect class="cls-1" x="291.53" y="188.867" width="213.048" height="171.445"/>
+  </g>
+</svg>
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diff --git a/docs/rl_docs.md b/docs/rl_docs.md
new file mode 100644
index 0000000..f2c38ee
--- /dev/null
+++ b/docs/rl_docs.md
@@ -0,0 +1,33 @@
+### Installation
+
+Create and activate a virtual environment and install as a package using
+
+```
+pip install .
+```
+
+### Running JUSTICE MOMARL
+
+#### Training
+
+JUSTICE-MOMARL can train multi-agent and multi-objective policies and currently support optimising for two objectives. JUSTICE-MOMARL converts the multi-objective training into a single objective by generating various policies with different weight combinations that weight each objective. The entire training process generates **100 different weight combinations** and trains each policy by linearising and normalising the reward and using **Multi Agent Proximal Policy Optimization (MAPPO)** as the reinforcement learning algorithm.
+
+Instead of training all the 100 weights together, we divide the training into batches and train a subset of weight combinations in each batch. For example, in order to train the first 10 uniform weight combnations, balancing the global economic output and inverse global temperature reward functions, run the following:
+
+```bash
+python train.py --start_uniform_weight 0 --end_uniform_weight 10 --env-config.rewards "global_economic_output" "inverse_global_temperature" --seed=<Seed> --base_save_path="Path where results are saved"
+```
+
+You can view the additional argument to modify the JUSTICE modules and the RL hyperparameters by looking at the `Args` and `EnvConfig` dataclasses in `rl/args.py`.
+
+This script will save the checkpoints, pickled configs and multi-objective vectorised returns for trained policies for each weight combination in the base_save_path provided.
+
+#### Evaluation
+
+Once models have been trained for different weight combinations and the checkpoints for each of them are saved, you can run the evaluation for one specific weight combination and for one evaluation seed by running:
+
+```bash
+python eval.py --checkpoint_path="Absolute path to the checkpoint for policy" --seed=0 --output_path="Directory where evaluation results are saved""
+```
+
+The evaluation script will run the MOMARL policy in the JUSTICE simulation and save the evolution of various economic and climate metrics over time where the Emission Control Rate and Savings Rates are set by the trained policy in each time step.
diff --git a/justice/model.py b/justice/model.py
index 61dac1e..8ee8463 100644
--- a/justice/model.py
+++ b/justice/model.py
@@ -98,8 +98,8 @@ def __init__(
         damage_function_type=DamageFunction.KALKUHL,
         abatement_type=Abatement.ENERDATA,
         social_welfare_function=WelfareFunction.UTILITARIAN,
-        clustering = False,
-        cluster_level = None,
+        clustering=False,
+        cluster_level=None,
         **kwargs,
     ):
         # If already initialized, do not repeat heavy initialization.
@@ -128,45 +128,46 @@ def __init__(
         # Load the datasets by instantiating the DataLoader class
         self.data_loader = DataLoader()
         self.region_list = self.data_loader.REGION_LIST
-        
+
         if self.clustering:
             if self.cluster_level == 12:
                 with open("data/input/rice_12_regions_dict.json") as f:
                     rice_json = json.load(f)
             elif self.cluster_level == 5:
-                with open("data/input/5_regions.json") as f:
+                with open("data/input/R5_regions.json") as f:
                     rice_json = json.load(f)
             else:
                 raise ValueError("Cluster level not supported")
-            
+
             with open("data/input/rice50_regions_dict.json") as f:
                 rice_50_json = json.load(f)
 
             self.clusters = list(rice_json.keys())
-            
+
             region_list = self.region_list.tolist()
 
             region_to_index = {region: idx for idx, region in enumerate(region_list)}
-            cluster_to_index = {cluster: idx for idx, cluster in enumerate(self.clusters)}
+            cluster_to_index = {
+                cluster: idx for idx, cluster in enumerate(self.clusters)
+            }
 
-            #create a mapping from region to cluster
+            # create a mapping from region to cluster
             self.country_to_cluster = {}
             for region, country_codes in rice_50_json.items():
-                region_index = region_to_index.get(region)  
+                region_index = region_to_index.get(region)
                 for code in country_codes:
                     for cluster, cluster_codes in rice_json.items():
                         if code in cluster_codes:
-                            cluster_index = cluster_to_index[cluster] 
+                            cluster_index = cluster_to_index[cluster]
                             self.country_to_cluster[region_index] = cluster_index
                             break  # Break the loop as soon as we find a match for efficiency
 
-            #create an inverse mapping from cluster to region
+            # create an inverse mapping from cluster to region
             self.cluster_to_country = {}
             for region, cluster in self.country_to_cluster.items():
                 if cluster not in self.cluster_to_country:
                     self.cluster_to_country[cluster] = []
                 self.cluster_to_country[cluster].append(region)
-                        
 
         # Instantiate the TimeHorizon class # TODO: Need to do the data slicing here for different start and end years
         self.time_horizon = TimeHorizon(
@@ -471,7 +472,10 @@ def stepwise_run(
         elif not self.clustering:
             self.savings_rate[:, timestep] = savings_rate
         else:
-            for key, value in self.country_to_cluster.items(): #[('region', 'cluster')]
+            for (
+                key,
+                value,
+            ) in self.country_to_cluster.items():  # [('region', 'cluster')]
                 self.savings_rate[key, timestep] = savings_rate[value]
             savings_rate = self.savings_rate[:, timestep]
 
@@ -480,15 +484,20 @@ def stepwise_run(
             emission_control_rate = np.tile(
                 emission_control_rate[:, np.newaxis], (1, self.no_of_ensembles)
             )
-            
+
         if not self.clustering:
             self.emission_control_rate[:, timestep, :] = emission_control_rate
 
         else:
-            for key, value in self.country_to_cluster.items(): #[('region', 'cluster')]
-                self.emission_control_rate[key, timestep, :] = emission_control_rate[value]
+            for (
+                key,
+                value,
+            ) in self.country_to_cluster.items():  # [('region', 'cluster')]
+                self.emission_control_rate[key, timestep, :] = emission_control_rate[
+                    value
+                ]
             emission_control_rate = self.emission_control_rate[:, timestep, :]
-            
+
         self.emission_control_rate[:, timestep, :] = emission_control_rate
 
         gross_output = self.economy.run(
diff --git a/justice/util/EMA_model_wrapper.py b/justice/util/EMA_model_wrapper.py
index 0679355..40e851b 100644
--- a/justice/util/EMA_model_wrapper.py
+++ b/justice/util/EMA_model_wrapper.py
@@ -14,8 +14,10 @@
     fraction_of_ensemble_above_threshold,
 )
 
+from justice.util.regional_configuration import aggregate_by_macro_region
 from justice.util.emission_control_constraint import EmissionControlConstraint
 
+
 # Scaling Values
 max_temperature = 16.0
 min_temperature = 0.0
@@ -52,7 +54,7 @@ def model_wrapper_emodps(**kwargs):
 
     rbf = RBF(
         n_rbfs=(n_inputs_rbf + 2), n_inputs=n_inputs_rbf, n_outputs=n_outputs_rbf
-    )  # n_inputs_rbf is a rule of thumb. Hasn't been verified yet for complex models
+    )  # n_inputs_rbf +2  is a rule of thumb.
 
     centers_shape, radii_shape, weights_shape = rbf.get_shape()
 
@@ -156,11 +158,6 @@ def model_wrapper_emodps(**kwargs):
     # Calculate the mean of ["welfare"] over the 1000 ensembles
     welfare = np.abs(datasets["welfare"])
 
-    # Get the years above temperature threshold
-    # years_above_threshold = years_above_temperature_threshold(
-    #     datasets["global_temperature"], 2.0
-    # )
-
     # Calculate the fraction of ensemble above the temperature threshold temperature, temperature_year_index, threshold
     fraction_above_threshold = fraction_of_ensemble_above_threshold(
         temperature=datasets["global_temperature"],
@@ -168,27 +165,467 @@ def model_wrapper_emodps(**kwargs):
         threshold=2.0,
     )
 
-    # TODO: Temporarily commented out for bi-objective optimization
+    return (
+        welfare,
+        fraction_above_threshold,
+    )
 
-    # Transform the damage cost per capita to welfare loss value
-    # _, _, _, welfare_loss_damage = model.welfare_function.calculate_welfare(
-    #     datasets["damage_cost_per_capita"], welfare_loss=True
-    # )
-    # welfare_loss_damage = np.abs(welfare_loss_damage)
 
-    # # Transform the abatement cost to welfare loss value
-    # _, _, _, welfare_loss_abatement = model.welfare_function.calculate_welfare(
-    #     datasets["abatement_cost_per_capita"], welfare_loss=True
-    # )
-    # welfare_loss_abatement = np.abs(welfare_loss_abatement)
+################################################################################################################################
+# --- Normalization helper ---------------------------------------------------------
+def _compute_inverse_range(min_value: float, max_value: float) -> float:
+    span = max_value - min_value
+    if span <= 0:
+        raise ValueError(
+            f"Invalid normalization bounds: min={min_value}, max={max_value}"
+        )
+    return 1.0 / span
+
+
+def _extract_vector(kwargs_dict, prefix, size, macro_idx):
+    """Pull a flat vector of length `size` from kwargs named `{prefix} {macro_idx} {i}`."""
+    vector = np.empty(size, dtype=float)
+    for i in range(size):
+        vector[i] = kwargs_dict.pop(f"{prefix} {macro_idx} {i}")
+    return vector
+
+
+# --- MOMA Wrapper ----------------------------------------------------------------------
+def model_wrapper_momadps(**kwargs):
+    scenario = kwargs.pop("ssp_rcp_scenario")
+    social_welfare_function_type = kwargs.pop("social_welfare_function_type")
+
+    economy_type = Economy.from_index(kwargs.pop("economy_type"))
+    damage_function_type = DamageFunction.from_index(kwargs.pop("damage_function_type"))
+    abatement_type = Abatement.from_index(kwargs.pop("abatement_type"))
+    stochastic_run = kwargs.pop("stochastic_run")
+
+    n_regions = kwargs.pop("n_regions")
+    n_timesteps = kwargs.pop("n_timesteps")
+    emission_control_start_timestep = kwargs.pop("emission_control_start_timestep")
+
+    n_inputs_rbf = kwargs.pop("n_inputs_rbf")  # should be 3
+    n_outputs_rbf = kwargs.pop("n_outputs_rbf")
+    temperature_year_of_interest_index = kwargs.pop(
+        "temperature_year_of_interest_index"
+    )
+    climate_ensemble_members = kwargs.pop("climate_ensemble_members")
+
+    # Normalization parameters (all provided via config)
+    min_temperature = kwargs.pop("min_temperature")
+    max_temperature = kwargs.pop("max_temperature")
+    min_temperature_change = kwargs.pop("min_temperature_change")
+    max_temperature_change = kwargs.pop("max_temperature_change")
+    consumption_min = kwargs.pop("consumption_min")
+    consumption_max = kwargs.pop("consumption_max")
+
+    inv_temperature_range = _compute_inverse_range(min_temperature, max_temperature)
+    inv_temperature_change_range = _compute_inverse_range(
+        min_temperature_change, max_temperature_change
+    )
+    inv_consumption_range = _compute_inverse_range(consumption_min, consumption_max)
+
+    # Macro-region setup
+    region_to_macro = np.asarray(kwargs.pop("region_to_macro"), dtype=np.intp)
+    n_macro_regions = kwargs.pop("n_macro_regions")
+
+    macro_region_counts = np.bincount(
+        region_to_macro, minlength=n_macro_regions
+    ).astype(float)
+    macro_region_counts = macro_region_counts[:, None]  # For broadcasting
+
+    # RBF instantiation: one per macro region, each with its own parameter block
+    rbf_template = RBF(
+        n_rbfs=(n_inputs_rbf + 2), n_inputs=n_inputs_rbf, n_outputs=n_outputs_rbf
+    )
+    centers_shape, radii_shape, weights_shape = rbf_template.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+
+    macro_rbfs = []
+    for macro_idx in range(n_macro_regions):
+        centers = _extract_vector(kwargs, "center", centers_len, macro_idx)
+        radii = _extract_vector(kwargs, "radii", radii_len, macro_idx)
+        weights = _extract_vector(kwargs, "weights", weights_len, macro_idx)
+
+        rbf = RBF(
+            n_rbfs=(n_inputs_rbf + 2), n_inputs=n_inputs_rbf, n_outputs=n_outputs_rbf
+        )
+        decision_vars = np.concatenate((centers, radii, weights))
+        rbf.set_decision_vars(decision_vars)
+        macro_rbfs.append(rbf)
+
+    emission_constraint = EmissionControlConstraint(
+        max_annual_growth_rate=0.04,
+        emission_control_start_timestep=emission_control_start_timestep,
+        min_emission_control_rate=0.01,
+    )
+
+    # --- Singleton JUSTICE handling -------------------------------------------------
+    if not hasattr(model_wrapper_momadps, "justice_instance"):
+        model_wrapper_momadps.justice_instance = JUSTICE(
+            scenario=scenario,
+            economy_type=economy_type,
+            damage_function_type=damage_function_type,
+            abatement_type=abatement_type,
+            social_welfare_function_type=social_welfare_function_type,
+            stochastic_run=stochastic_run,
+            climate_ensembles=climate_ensemble_members,
+        )
+    else:
+        model_wrapper_momadps.justice_instance.reset_model()
+
+    model = model_wrapper_momadps.justice_instance
+    no_of_ensembles = model.__getattribute__("no_of_ensembles")
+
+    # --- Policy buffers -------------------------------------------------------------
+    regional_emission_control_rate = np.zeros(
+        (n_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+    constrained_emission_control_rate = np.zeros_like(regional_emission_control_rate)
+    macro_emission_control_rate = np.zeros(
+        (n_macro_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+
+    # Feedback state
+    previous_temperature = np.zeros(no_of_ensembles, dtype=float)
+    previous_temperature_change = np.zeros(no_of_ensembles, dtype=float)
+
+    # Temporary buffer reused for RBF inputs
+    rbf_input_buffer = np.empty((n_inputs_rbf, no_of_ensembles), dtype=float)
+
+    # Storage for macro-level consumption history
+    macro_consumption_per_capita_history = np.zeros(
+        (n_macro_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+
+    population = model.economy.get_population()  # Getting population from the model
+    population = aggregate_by_macro_region(
+        population, region_to_macro
+    )  # Aggregating to macro regions
+    # --- Simulation loop ------------------------------------------------------------
+    for timestep in range(n_timesteps):
+        constrained_emission_control_rate[:, timestep, :] = (
+            emission_constraint.constrain_emission_control_rate(
+                regional_emission_control_rate[:, timestep, :],
+                timestep,
+                allow_fallback=False,
+            )
+        )
+
+        model.stepwise_run(
+            emission_control_rate=constrained_emission_control_rate[:, timestep, :],
+            timestep=timestep,
+            endogenous_savings_rate=True,
+        )
+        datasets = model.stepwise_evaluate(timestep=timestep)
+
+        global_temperature = datasets["global_temperature"][timestep, :]
+
+        consumption = datasets["consumption"][:, timestep, :]
+        consumption = consumption * 1e3
+
+        # Temperature and rate of change (shared inputs)
+        if timestep == 0:
+            temperature_change = np.zeros_like(global_temperature)
+            previous_temperature = global_temperature.copy()
+            previous_temperature_change = temperature_change.copy()
+        elif timestep % 5 == 0:
+            temperature_change = global_temperature - previous_temperature
+            previous_temperature = global_temperature.copy()
+            previous_temperature_change = temperature_change.copy()
+        else:
+            temperature_change = previous_temperature_change
+
+        rbf_input_buffer[0, :] = np.clip(
+            (global_temperature - min_temperature) * inv_temperature_range, 0.0, 1.0
+        )
+        rbf_input_buffer[1, :] = np.clip(
+            (temperature_change - min_temperature_change)
+            * inv_temperature_change_range,
+            0.0,
+            1.0,
+        )
+
+        # Aggregate consumption to macro level, normalize, and store full history
+        aggregated_consumption = aggregate_by_macro_region(  # Use consumption here
+            consumption, region_to_macro
+        )
+
+        aggregated_consumption_per_capita = (
+            aggregated_consumption / population[:, timestep, :]
+        )
+
+        macro_consumption_per_capita_history[:, timestep, :] = (
+            aggregated_consumption_per_capita
+        )
+
+        normalized_aggregated_consumption_per_capita = (
+            np.clip(  # TODO Check this normalization
+                (aggregated_consumption_per_capita - consumption_min)
+                * inv_consumption_range,
+                0.0,
+                1.0,
+            )
+        )
+
+        # RBFs observe current step signals and set emissions for next step
+        if timestep < n_timesteps - 1:
+            for macro_idx, rbf in enumerate(macro_rbfs):
+                rbf_input_buffer[2, :] = normalized_aggregated_consumption_per_capita[
+                    macro_idx, :
+                ]
+                macro_output = rbf.apply_rbfs(rbf_input_buffer)
+                macro_emission_control_rate[macro_idx, timestep + 1, :] = macro_output
+
+            regional_emission_control_rate[:, timestep + 1, :] = (
+                macro_emission_control_rate[region_to_macro, timestep + 1, :]
+            )
+
+    datasets = model.evaluate()
+
+    spatially_disaggregated_welfare = (
+        model.welfare_function.calculate_spatially_disaggregated_welfare(
+            macro_consumption_per_capita_history
+        )
+    )
+
+    fraction_above_threshold = fraction_of_ensemble_above_threshold(
+        temperature=datasets["global_temperature"],
+        temperature_year_index=temperature_year_of_interest_index,
+        threshold=2.0,
+    )
 
     return (
-        welfare,
-        fraction_above_threshold,
-        # years_above_threshold,
-        # welfare_loss_damage,
-        # welfare_loss_abatement,
-    )  # ,
+        float(spatially_disaggregated_welfare[0]),
+        float(spatially_disaggregated_welfare[1]),
+        float(spatially_disaggregated_welfare[2]),
+        float(spatially_disaggregated_welfare[3]),
+        float(spatially_disaggregated_welfare[4]),
+        float(fraction_above_threshold),
+    )
+
+
+# ---------------------------MOMA Single Agent----------------------------------------------------------------#
+
+
+def model_wrapper_momadps_single_agent(**kwargs):
+    """
+    Variant of the MOMADPS wrapper for single-agent re-optimization.
+    Only the RBF of `variable_macro_index` is free to change; all others are fixed
+    to a prior (Pareto-Nash) solution supplied via fixed_centers/radii/weights.
+    """
+    scenario = kwargs.pop("ssp_rcp_scenario")
+    social_welfare_function_type = kwargs.pop("social_welfare_function_type")
+
+    economy_type = Economy.from_index(kwargs.pop("economy_type"))
+    damage_function_type = DamageFunction.from_index(kwargs.pop("damage_function_type"))
+    abatement_type = Abatement.from_index(kwargs.pop("abatement_type"))
+    stochastic_run = kwargs.pop("stochastic_run")
+
+    n_regions = kwargs.pop("n_regions")
+    n_timesteps = kwargs.pop("n_timesteps")
+    emission_control_start_timestep = kwargs.pop("emission_control_start_timestep")
+
+    n_inputs_rbf = kwargs.pop("n_inputs_rbf")
+    n_outputs_rbf = kwargs.pop("n_outputs_rbf")
+    temperature_year_of_interest_index = kwargs.pop(
+        "temperature_year_of_interest_index"
+    )
+    climate_ensemble_members = kwargs.pop("climate_ensemble_members")
+
+    # Normalization parameters
+    min_temperature = kwargs.pop("min_temperature")
+    max_temperature = kwargs.pop("max_temperature")
+    min_temperature_change = kwargs.pop("min_temperature_change")
+    max_temperature_change = kwargs.pop("max_temperature_change")
+    consumption_min = kwargs.pop("consumption_min")
+    consumption_max = kwargs.pop("consumption_max")
+
+    inv_temperature_range = _compute_inverse_range(min_temperature, max_temperature)
+    inv_temperature_change_range = _compute_inverse_range(
+        min_temperature_change, max_temperature_change
+    )
+    inv_consumption_range = _compute_inverse_range(consumption_min, consumption_max)
+
+    # Macro-region setup
+    region_to_macro = np.asarray(kwargs.pop("region_to_macro"), dtype=np.intp)
+    n_macro_regions = kwargs.pop("n_macro_regions")
+
+    # Which macro region is being (re)optimized
+    variable_macro_index = kwargs.pop("variable_macro_index")
+
+    # Fixed RBF parameters from the Pareto-Nash solution (constants)
+    fixed_centers = np.asarray(kwargs.pop("fixed_centers"), dtype=float)
+    fixed_radii = np.asarray(kwargs.pop("fixed_radii"), dtype=float)
+    fixed_weights = np.asarray(kwargs.pop("fixed_weights"), dtype=float)
+
+    macro_region_counts = np.bincount(
+        region_to_macro, minlength=n_macro_regions
+    ).astype(float)
+    macro_region_counts = macro_region_counts[:, None]
+
+    # RBF parameter shapes
+    rbf_template = RBF(
+        n_rbfs=(n_inputs_rbf + 2), n_inputs=n_inputs_rbf, n_outputs=n_outputs_rbf
+    )
+    centers_shape, radii_shape, weights_shape = rbf_template.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+
+    # Build the RBF set: only one macro uses lever values; others use fixed
+    macro_rbfs = []
+    for macro_idx in range(n_macro_regions):
+        if macro_idx == variable_macro_index:
+            centers = _extract_vector(kwargs, "center", centers_len, macro_idx)
+            radii = _extract_vector(kwargs, "radii", radii_len, macro_idx)
+            weights = _extract_vector(kwargs, "weights", weights_len, macro_idx)
+        else:
+            centers = fixed_centers[macro_idx]
+            radii = fixed_radii[macro_idx]
+            weights = fixed_weights[macro_idx]
+
+        rbf = RBF(
+            n_rbfs=(n_inputs_rbf + 2), n_inputs=n_inputs_rbf, n_outputs=n_outputs_rbf
+        )
+        decision_vars = np.concatenate((centers, radii, weights))
+        rbf.set_decision_vars(decision_vars)
+        macro_rbfs.append(rbf)
+
+    emission_constraint = EmissionControlConstraint(
+        max_annual_growth_rate=0.04,
+        emission_control_start_timestep=emission_control_start_timestep,
+        min_emission_control_rate=0.01,
+    )
+
+    # JUSTICE singleton handling
+    if not hasattr(model_wrapper_momadps_single_agent, "justice_instance"):
+        model_wrapper_momadps_single_agent.justice_instance = JUSTICE(
+            scenario=scenario,
+            economy_type=economy_type,
+            damage_function_type=damage_function_type,
+            abatement_type=abatement_type,
+            social_welfare_function_type=social_welfare_function_type,
+            stochastic_run=stochastic_run,
+            climate_ensembles=climate_ensemble_members,
+        )
+    else:
+        model_wrapper_momadps_single_agent.justice_instance.reset_model()
+
+    model = model_wrapper_momadps_single_agent.justice_instance
+    no_of_ensembles = model.no_of_ensembles
+
+    # Buffers
+    regional_emission_control_rate = np.zeros(
+        (n_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+    constrained_emission_control_rate = np.zeros_like(regional_emission_control_rate)
+    macro_emission_control_rate = np.zeros(
+        (n_macro_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+
+    previous_temperature = np.zeros(no_of_ensembles, dtype=float)
+    previous_temperature_change = np.zeros(no_of_ensembles, dtype=float)
+
+    rbf_input_buffer = np.empty((n_inputs_rbf, no_of_ensembles), dtype=float)
+    macro_consumption_per_capita_history = np.zeros(
+        (n_macro_regions, n_timesteps, no_of_ensembles), dtype=float
+    )
+
+    population = model.economy.get_population()
+    population = aggregate_by_macro_region(population, region_to_macro)
+
+    # Simulation loop
+    for timestep in range(n_timesteps):
+        constrained_emission_control_rate[:, timestep, :] = (
+            emission_constraint.constrain_emission_control_rate(
+                regional_emission_control_rate[:, timestep, :],
+                timestep,
+                allow_fallback=False,
+            )
+        )
+
+        model.stepwise_run(
+            emission_control_rate=constrained_emission_control_rate[:, timestep, :],
+            timestep=timestep,
+            endogenous_savings_rate=True,
+        )
+        datasets = model.stepwise_evaluate(timestep=timestep)
+
+        global_temperature = datasets["global_temperature"][timestep, :]
+        consumption = datasets["consumption"][:, timestep, :] * 1e3
+
+        if timestep == 0:
+            temperature_change = np.zeros_like(global_temperature)
+            previous_temperature = global_temperature.copy()
+            previous_temperature_change = temperature_change.copy()
+        elif timestep % 5 == 0:
+            temperature_change = global_temperature - previous_temperature
+            previous_temperature = global_temperature.copy()
+            previous_temperature_change = temperature_change.copy()
+        else:
+            temperature_change = previous_temperature_change
+
+        rbf_input_buffer[0, :] = np.clip(
+            (global_temperature - min_temperature) * inv_temperature_range, 0.0, 1.0
+        )
+        rbf_input_buffer[1, :] = np.clip(
+            (temperature_change - min_temperature_change)
+            * inv_temperature_change_range,
+            0.0,
+            1.0,
+        )
+
+        aggregated_consumption = aggregate_by_macro_region(consumption, region_to_macro)
+        aggregated_consumption_per_capita = (
+            aggregated_consumption / population[:, timestep, :]
+        )
+        macro_consumption_per_capita_history[:, timestep, :] = (
+            aggregated_consumption_per_capita
+        )
+        normalized_consumption = np.clip(
+            (aggregated_consumption_per_capita - consumption_min)
+            * inv_consumption_range,
+            0.0,
+            1.0,
+        )
+
+        if timestep < n_timesteps - 1:
+            for macro_idx, rbf in enumerate(macro_rbfs):
+                rbf_input_buffer[2, :] = normalized_consumption[macro_idx, :]
+                macro_output = rbf.apply_rbfs(rbf_input_buffer)
+                macro_emission_control_rate[macro_idx, timestep + 1, :] = macro_output
+
+            regional_emission_control_rate[:, timestep + 1, :] = (
+                macro_emission_control_rate[region_to_macro, timestep + 1, :]
+            )
+
+    datasets = model.evaluate()
+
+    spatial_welfare = model.welfare_function.calculate_spatially_disaggregated_welfare(
+        macro_consumption_per_capita_history
+    )
+    agent_welfare = float(spatial_welfare[variable_macro_index])
+
+    fraction_above_threshold = fraction_of_ensemble_above_threshold(
+        temperature=datasets["global_temperature"],
+        temperature_year_index=temperature_year_of_interest_index,
+        threshold=2.0,
+    )
+
+    return (
+        agent_welfare,
+        float(fraction_above_threshold),
+    )
+
+
+##################################################################################################################################
 
 
 def model_wrapper(**kwargs):
diff --git a/justice/util/borg_archive_processor.py b/justice/util/borg_archive_processor.py
new file mode 100644
index 0000000..557d7e6
--- /dev/null
+++ b/justice/util/borg_archive_processor.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""
+Convert per-island CSV snapshots (mm_intermediate/mm_*) into tar.gz archives that
+mimic the original EMA structure: a tmp/ folder containing selected CSV files.
+
+Usage:
+    python borg_archive_processor.py \
+        --archive /path_to_data/mm_intermediate.zip \
+        --base-name UTILITARIAN_50000_17 \
+        --step 1000 \
+        --output-dir /path/to/save/tars
+
+
+If --archive points to a directory (e.g., mm_intermediate/) instead of a zip file,
+the script works the same way.
+"""
+
+
+import argparse
+import os
+import shutil
+import tarfile
+import tempfile
+import zipfile
+from pathlib import Path
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Package mm_* snapshots into tmp/ tarballs."
+    )
+    parser.add_argument(
+        "--archive",
+        required=True,
+        help="Path to mm_intermediate.zip or an extracted mm_intermediate directory.",
+    )
+    parser.add_argument(
+        "--base-name",
+        required=True,
+        help="Base name for outputs (e.g., UTILITARIAN_50000_17). "
+        "Each island tar will be {base-name}_{island}.tar.gz",
+    )
+    parser.add_argument(
+        "--step",
+        type=int,
+        default=0,
+        help="Only keep CSV files whose numeric name is a multiple of this step. "
+        "Use 0 (default) to keep every CSV in the island folder.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default=None,
+        help="Directory for the output tar.gz files. Default: same as the archive location.",
+    )
+    return parser.parse_args()
+
+
+def extract_if_needed(archive_path: Path) -> Path:
+    """If archive_path is a zip file, extract it to a temp dir and return the mm_intermediate folder.
+    Otherwise, assume it already points to the mm_intermediate directory."""
+    if archive_path.is_file() and archive_path.suffix == ".zip":
+        temp_dir = Path(tempfile.mkdtemp(prefix="mm_intermediate_"))
+        with zipfile.ZipFile(archive_path, "r") as zf:
+            zf.extractall(temp_dir)
+        subdirs = [p for p in temp_dir.glob("*") if p.is_dir()]
+        if len(subdirs) == 1:
+            extracted_root = subdirs[0]
+        else:
+            extracted_root = temp_dir / "mm_intermediate"
+        return extracted_root
+    else:
+        return archive_path
+
+
+def integer_basename(path: Path):
+    try:
+        return int(path.stem)
+    except ValueError:
+        return None
+
+
+def select_csvs(csv_files, step):
+    """Always include the first CSV, then apply the step filter (if step > 0)."""
+    if not csv_files:
+        return []
+
+    selected = []
+    first_csv = csv_files[0]
+    selected.append(first_csv)
+
+    if step <= 0:
+        selected.extend(csv_files[1:])
+        return selected
+
+    for csv_file in csv_files[1:]:
+        nfe = integer_basename(csv_file)
+        if nfe is None:
+            continue
+        if nfe % step == 0:
+            selected.append(csv_file)
+
+    # Ensure no duplicates and preserve ordering
+    seen = set()
+    unique_selected = []
+    for csv_file in selected:
+        if csv_file not in seen:
+            unique_selected.append(csv_file)
+            seen.add(csv_file)
+
+    return unique_selected
+
+
+def package_mm_island(island_dir, output_dir, base_name, island_idx, step):
+    csv_files = sorted(
+        [f for f in island_dir.glob("*.csv") if integer_basename(f) is not None],
+        key=lambda f: integer_basename(f),
+    )
+    if not csv_files:
+        print(f"[WARN] No numeric CSV files found in {island_dir}")
+        return None
+
+    chosen = select_csvs(csv_files, step)
+    if not chosen:
+        print(f"[WARN] Step filter removed all CSVs in {island_dir}; nothing to pack.")
+        return None
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    archive_name = f"{base_name}_{island_idx}.tar.gz"
+    tar_path = output_dir / archive_name
+
+    with tempfile.TemporaryDirectory(prefix=f"tmp_mm_{island_idx}_") as tmp_root:
+        tmp_dir = Path(tmp_root) / "tmp"
+        tmp_dir.mkdir()
+        for csv_file in chosen:
+            shutil.copy2(csv_file, tmp_dir / csv_file.name)
+        with tarfile.open(tar_path, "w:gz") as tar:
+            tar.add(tmp_dir, arcname="tmp")
+
+    print(f"[OK] {tar_path}")
+    return tar_path
+
+
+def main():
+    args = parse_args()
+    archive_path = Path(args.archive)
+    root = extract_if_needed(archive_path)
+
+    if not root.exists():
+        raise FileNotFoundError(
+            f"Folder {root} not found (archive may have no mm_intermediate folder)."
+        )
+
+    island_dirs = sorted(
+        [p for p in root.iterdir() if p.is_dir() and p.name.startswith("mm_")]
+    )
+    if not island_dirs:
+        raise RuntimeError(f"No island subdirectories (mm_*) found under {root}")
+
+    output_dir = Path(args.output_dir) if args.output_dir else archive_path.parent
+
+    print(f"Processing {len(island_dirs)} island folders under {root}")
+    print(f"Step filter: {'all CSVs' if args.step <= 0 else f'every {args.step}'}")
+
+    for island_dir in island_dirs:
+        suffix = island_dir.name.split("mm_")[-1]
+        package_mm_island(
+            island_dir=island_dir,
+            output_dir=output_dir,
+            base_name=args.base_name,
+            island_idx=suffix,
+            step=max(args.step, 0),
+        )
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/justice/util/enumerations.py b/justice/util/enumerations.py
index caee4fe..e508ef8 100644
--- a/justice/util/enumerations.py
+++ b/justice/util/enumerations.py
@@ -36,7 +36,6 @@ class SSP(IntEnum):
     SSP5 = 4
 
 
-# TODO: Add the pretty strings in the tuple like (0, SSP.SSP1, "ssp119", "SSP1-RCP1.9")
 class Scenario(Enum):
     SSP119 = (0, SSP.SSP1, "ssp119", "SSP1-RCP1.9")
     SSP126 = (1, SSP.SSP1, "ssp126", "SSP1-RCP2.6")
@@ -149,8 +148,9 @@ class Optimizer(Enum):
     """
 
     EpsNSGAII = 0
-    BorgMOEA = 1
+    MMBorgMOEA = 1
     MOMARL = 2
+    MSBorgMOEA = 3
 
     @staticmethod
     def from_index(index):
@@ -197,4 +197,4 @@ def from_index(index):
         for enum in Rewards:
             if enum.value[0] == index:
                 return enum
-        return None
\ No newline at end of file
+        return None
diff --git a/justice/util/feature_importance.py b/justice/util/feature_importance.py
new file mode 100644
index 0000000..44c21ba
--- /dev/null
+++ b/justice/util/feature_importance.py
@@ -0,0 +1,692 @@
+import json
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from catboost import CatBoostRegressor, Pool
+from sklearn.model_selection import KFold
+
+from justice.util.model_time import TimeHorizon
+from justice.util.data_loader import DataLoader
+from justice.util.visualizer import justice_region_aggregator
+
+
+# -------------------------------
+# Aggregation helper (returning years, inclusive slicing)
+# -------------------------------
+def aggregate_abated_emissions(
+    input_data_arrays,
+    region_mapping_path,
+    rice_region_dict_path,  # kept for API compatibility
+    start_year,
+    end_year,
+    splice_start_year,
+    splice_end_year,
+    data_timestep=5,
+    timestep=1,
+):
+    """
+    Aggregate abated emissions to 12 regions and slice to [splice_start_year, splice_end_year] inclusively.
+    Returns: (aggregated_array, region_list, years_vector)
+      - aggregated_array shape: (12, T, S) regions x years x samples
+      - years_vector: numpy array of years of length T
+    """
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    data_loader = DataLoader()
+
+    with open(region_mapping_path, "r") as f:
+        region_mapping_json = json.load(f)
+
+    # Often used internally by your stack; load to ensure availability
+    with open(rice_region_dict_path, "r") as f:
+        _ = json.load(f)
+
+    last_agg = None
+    region_list = None
+
+    for economic_data in input_data_arrays:
+        # Aggregate to 12 regions
+        region_list, economic_data_aggregated = justice_region_aggregator(
+            data_loader, region_mapping_json, economic_data
+        )
+
+        # Inclusive end-year slice (ensure 2100 is included if requested)
+        start_idx = time_horizon.year_to_timestep(splice_start_year, timestep=timestep)
+        end_idx = (
+            time_horizon.year_to_timestep(splice_end_year, timestep=timestep) + 1
+        )  # inclusive
+
+        economic_data_aggregated = np.asarray(economic_data_aggregated)[
+            :, start_idx:end_idx, :
+        ]
+        last_agg = economic_data_aggregated
+
+    years = np.arange(splice_start_year, splice_end_year + 1, timestep)
+    return last_agg, region_list, years
+
+
+# -------------------------------
+# Build long DataFrame (regional + global, keep samples)
+# -------------------------------
+def build_long_dataframe(
+    base_path="data/temporary/NU_DATA/mmBorg/",
+    region_mapping_path="data/input/12_regions.json",
+    rice_region_dict_path="data/input/rice50_regions_dict.json",
+    years_of_interest=(2030, 2050, 2070, 2100),
+):
+    """
+    Build a long/tidy DataFrame with columns:
+    Optimization, Regret, Scenario, Welfare, Region, Year, Sample, AbatedEmission, Scope
+    Includes both Regional and Global (summed across regions) entries for selected years.
+    """
+    scenario_list = ["SSP126", "SSP245", "SSP370", "SSP460", "SSP534"]
+
+    with open(base_path + "min_regret_policy_indices.json", "r") as f:
+        policy_indices = json.load(f)
+
+    baseline_path = Path(base_path) / "emissions_array_all_SSPs.npy"
+    baseline_emissions = np.load(
+        baseline_path
+    )  # shape expected: (src_regions, time, scenarios)
+
+    rows = []
+
+    for reference_scenario, welfare_map in policy_indices.items():
+
+        for welfare_type, regret_map in welfare_map.items():
+
+            for scenario_index, scenario in enumerate(scenario_list):
+
+                for regret in regret_map.keys():
+                    policy_idx = regret_map[regret]
+                    folder_name = f"{welfare_type}_{reference_scenario}/ref_{reference_scenario}_{regret}_idx{policy_idx}"
+                    path = Path(base_path) / folder_name
+                    emissions_file = (
+                        path
+                        / f"{welfare_type}_ref_{reference_scenario}_{regret}_idx{policy_idx}_emissions_idx{policy_idx}_{scenario}_emissions.npy"
+                    )
+                    emissions_data = np.load(
+                        emissions_file
+                    )  # (src_regions, time, samples)
+
+                    # Abated emissions relative to baseline
+                    abated_emissions = (
+                        baseline_emissions[:, :, scenario_index][:, :, np.newaxis]
+                        - emissions_data
+                    )
+
+                    # Aggregate to 12 regions and slice years inclusively
+                    region_agg_arr, region_list, agg_years = aggregate_abated_emissions(
+                        input_data_arrays=[abated_emissions],
+                        region_mapping_path=region_mapping_path,
+                        rice_region_dict_path=rice_region_dict_path,
+                        start_year=2015,
+                        end_year=2300,
+                        splice_start_year=2025,
+                        splice_end_year=2100,  # inclusive in helper
+                        data_timestep=5,
+                        timestep=1,
+                    )
+                    # region_agg_arr: (12, T, samples)
+
+                    # Select only desired years
+                    year_mask = np.isin(agg_years, years_of_interest)
+                    if not np.any(year_mask):
+                        continue
+                    years_sel = agg_years[year_mask]
+                    arr_sel = region_agg_arr[
+                        :, year_mask, :
+                    ]  # (12, len(years_sel), samples)
+
+                    n_regions, n_years, n_samples = arr_sel.shape
+
+                    # Regional rows
+                    vals = arr_sel.reshape(-1)  # flatten: region->year->sample
+                    region_rep = np.repeat(region_list, n_years * n_samples)
+                    year_rep = np.tile(np.repeat(years_sel, n_samples), n_regions)
+                    sample_rep = np.tile(np.arange(n_samples), n_regions * n_years)
+
+                    rows.append(
+                        pd.DataFrame(
+                            {
+                                "Optimization": reference_scenario,
+                                "Regret": regret,
+                                "Scenario": scenario,
+                                "Welfare": welfare_type,
+                                "Region": region_rep,
+                                "Year": year_rep,
+                                "Sample": sample_rep,
+                                "AbatedEmission": vals.astype(np.float64),
+                                "Scope": "Regional",
+                            }
+                        )
+                    )
+
+                    # Global rows (sum across regions, per year and sample)
+                    global_arr = arr_sel.sum(axis=0)  # (n_years, n_samples)
+                    g_vals = global_arr.reshape(-1)
+                    g_year_rep = np.repeat(years_sel, n_samples)
+                    g_sample_rep = np.tile(np.arange(n_samples), n_years)
+
+                    rows.append(
+                        pd.DataFrame(
+                            {
+                                "Optimization": reference_scenario,
+                                "Regret": regret,
+                                "Scenario": scenario,
+                                "Welfare": welfare_type,
+                                "Region": "Global",
+                                "Year": g_year_rep,
+                                "Sample": g_sample_rep,
+                                "AbatedEmission": g_vals.astype(np.float64),
+                                "Scope": "Global",
+                            }
+                        )
+                    )
+
+    long_df = pd.concat(rows, ignore_index=True)
+
+    # Optimize dtypes
+    for col in ["Optimization", "Regret", "Scenario", "Welfare", "Region", "Scope"]:
+        long_df[col] = long_df[col].astype("category")
+    long_df["Year"] = long_df["Year"].astype(np.int32)
+    long_df["Sample"] = long_df["Sample"].astype(np.int32)
+
+    return long_df
+
+
+# -------------------------------
+# Build cell-level datasets (raw/mean/median/P90 targets)
+# -------------------------------
+def build_cell_level_targets(
+    long_df, years=(2030, 2050, 2070, 2100), target_stat="mean", scope="Global"
+):
+    """
+    Aggregate across 1001 samples to get one scalar target per cell, unless target_stat='raw'
+    in which case each sample is kept.
+    """
+    assert scope in ("Global", "Regional")
+    stat = target_stat.lower()
+    if stat == "p50":
+        stat = "median"
+
+    df = long_df[long_df["Year"].isin(years)].copy()
+
+    if stat == "raw":
+        if scope == "Global":
+            df = df[df["Region"] == "Global"]
+            keep_cols = [
+                "Year",
+                "Optimization",
+                "Regret",
+                "Scenario",
+                "Welfare",
+                "Sample",
+                "AbatedEmission",
+            ]
+        else:
+            df = df[df["Scope"] == "Regional"]
+            keep_cols = [
+                "Year",
+                "Region",
+                "Optimization",
+                "Regret",
+                "Scenario",
+                "Welfare",
+                "Sample",
+                "AbatedEmission",
+            ]
+        df = df[keep_cols].rename(columns={"AbatedEmission": "Y"})
+        for c in ["Optimization", "Regret", "Scenario", "Welfare"]:
+            df[c] = df[c].astype("category")
+        if scope == "Regional":
+            df["Region"] = df["Region"].astype("category")
+        df["Sample"] = df["Sample"].astype("category")
+        df["Year"] = df["Year"].astype(np.int32)
+        return df
+
+    if scope == "Global":
+        df = df[df["Region"] == "Global"]
+        group_cols = ["Year", "Optimization", "Regret", "Scenario", "Welfare"]
+    else:
+        df = df[df["Scope"] == "Regional"]
+        group_cols = ["Year", "Region", "Optimization", "Regret", "Scenario", "Welfare"]
+
+    if stat == "mean":
+        agg_df = (
+            df.groupby(group_cols, observed=True)["AbatedEmission"].mean().reset_index()
+        )
+    elif stat == "median":
+        agg_df = (
+            df.groupby(group_cols, observed=True)["AbatedEmission"]
+            .median()
+            .reset_index()
+        )
+    elif stat == "p90":
+        agg_df = (
+            df.groupby(group_cols, observed=True)["AbatedEmission"]
+            .quantile(0.90)
+            .reset_index()
+        )
+    else:
+        raise ValueError(
+            "target_stat must be one of: 'raw', 'mean', 'median' (or 'p50'), 'p90'"
+        )
+
+    agg_df = agg_df.rename(columns={"AbatedEmission": "Y"})
+    # Ensure categoricals
+    for c in ["Optimization", "Regret", "Scenario", "Welfare"]:
+        agg_df[c] = agg_df[c].astype("category")
+    if scope == "Regional":
+        agg_df["Region"] = agg_df["Region"].astype("category")
+    agg_df["Year"] = agg_df["Year"].astype(np.int32)
+    return agg_df
+
+
+# -------------------------------
+# CatBoost + SHAP with cross-validation and early stopping
+# -------------------------------
+def _loss_for_stat(target_stat):
+    stat = target_stat.lower()
+    if stat in ("mean", "raw"):
+        return "RMSE"
+    if stat in ("median", "p50"):
+        return "Quantile:alpha=0.5"
+    if stat == "p90":
+        return "Quantile:alpha=0.9"
+    raise ValueError("Unknown target_stat")
+
+
+def _metrics_for_stat(y_true, y_pred, target_stat):
+    """
+    Compute evaluation metrics per fold.
+    - For mean/raw (RMSE): return {'RMSE': ..., 'R2': ...}
+    - For median/P90 (Quantile): return {'Pinball': ...} where alpha is 0.5 or 0.9
+    """
+    y_true = np.asarray(y_true, dtype=float)
+    y_pred = np.asarray(y_pred, dtype=float)
+    stat = target_stat.lower()
+
+    def r2_score(y, yhat):
+        ss_res = np.sum((y - yhat) ** 2)
+        ss_tot = np.sum((y - np.mean(y)) ** 2)
+        return 0.0 if ss_tot <= 1e-12 else 1.0 - ss_res / ss_tot
+
+    if stat in ("mean", "raw"):
+        rmse = float(np.sqrt(np.mean((y_true - y_pred) ** 2)))
+        r2 = float(r2_score(y_true, y_pred))
+        return {"RMSE": rmse, "R2": r2}
+
+    # Quantile pinball loss
+    alpha = 0.5 if stat in ("median", "p50") else 0.9
+    e = y_true - y_pred
+    pinball = np.mean(np.maximum(alpha * e, (alpha - 1.0) * e))
+    return {"Pinball": float(pinball)}
+
+
+def _cat_indices(X, categorical_cols):
+    return [X.columns.get_loc(c) for c in categorical_cols]
+
+
+def shap_values_mean_abs(model, X, categorical_cols):
+    """
+    Mean absolute SHAP values per feature using CatBoost's SHAP.
+    """
+    pool = Pool(X, cat_features=_cat_indices(X, categorical_cols))
+    shap_vals = model.get_feature_importance(data=pool, type="ShapValues")
+    # shap_vals shape: (n_samples, n_features + 1); last column is expected value
+    shap_main = np.abs(shap_vals[:, :-1])
+    means = shap_main.mean(axis=0)
+    return pd.Series(means, index=X.columns).sort_values(ascending=False)
+
+
+def fit_catboost_cv_shap(
+    X,
+    y,
+    categorical_cols,
+    target_stat="mean",
+    cv_folds=5,
+    random_state=42,
+    params=None,
+):
+    """
+    Cross-validated CatBoost with early stopping.
+    - Trains a model per fold with eval_set for early stopping
+    - Collects SHAP importances on each validation fold, averages across folds
+    - Returns:
+        {
+          'cv_metrics': list of dicts per fold,
+          'cv_metrics_mean': dict of mean metrics,
+          'best_iterations': list,
+          'shap_cv_mean': Series,
+          'model_full': fitted CatBoost on full data,
+          'shap_full': Series
+        }
+    """
+    if params is None:
+        params = dict(
+            depth=6,
+            learning_rate=0.05,
+            n_estimators=800,
+            l2_leaf_reg=3.0,
+            random_seed=random_state,
+            loss_function=_loss_for_stat(target_stat),
+            od_type="Iter",
+            od_wait=50,  # early stopping patience
+            use_best_model=True,  # keep best iteration wrt eval_set
+            verbose=False,
+            allow_writing_files=False,
+        )
+
+    k = min(cv_folds, max(2, len(X)))  # ensure at least 2, at most N
+    kf = KFold(n_splits=k, shuffle=True, random_state=random_state)
+
+    cv_metrics = []
+    best_iters = []
+    shap_series_list = []
+
+    cat_idx = _cat_indices(X, categorical_cols)
+
+    for fold, (tr_idx, va_idx) in enumerate(kf.split(X), start=1):
+        X_tr, X_va = X.iloc[tr_idx], X.iloc[va_idx]
+        y_tr, y_va = y.iloc[tr_idx], y.iloc[va_idx]
+
+        train_pool = Pool(X_tr, y_tr, cat_features=cat_idx)
+        valid_pool = Pool(X_va, y_va, cat_features=cat_idx)
+
+        model = CatBoostRegressor(**params)
+        model.fit(train_pool, eval_set=valid_pool)
+
+        # Evaluate on validation
+        yhat_va = model.predict(valid_pool)
+        fold_metrics = _metrics_for_stat(y_va.values, yhat_va, target_stat)
+        cv_metrics.append(fold_metrics)
+
+        # Record best iteration if available
+        best_iter = getattr(model, "tree_count_", None)
+        if best_iter is None:
+            # Fallback: params n_estimators
+            best_iter = int(params.get("n_estimators", 800))
+        best_iters.append(best_iter)
+
+        # SHAP on validation fold, mean absolute
+        shap_series = shap_values_mean_abs(model, X_va, categorical_cols)
+        shap_series_list.append(shap_series)
+
+    # Aggregate metrics
+    metric_keys = cv_metrics[0].keys()
+    cv_metrics_mean = {
+        k: float(np.mean([m[k] for m in cv_metrics])) for k in metric_keys
+    }
+
+    # Average SHAP across folds (align by feature name)
+    shap_cv_df = pd.concat(shap_series_list, axis=1)
+    shap_cv_mean = shap_cv_df.mean(axis=1).sort_values(ascending=False)
+
+    # Fit final model on full data using average best_iters (rounded)
+    avg_best = int(
+        np.clip(np.round(np.mean(best_iters)), 50, params.get("n_estimators", 800))
+    )
+    final_params = dict(params)
+    final_params["n_estimators"] = avg_best
+    # disable use_best_model and overfitting detector for the final fit
+    final_params["use_best_model"] = False
+    final_params.pop("od_type", None)
+    final_params.pop("od_wait", None)
+
+    final_model = CatBoostRegressor(**final_params)
+    final_model.fit(Pool(X, y, cat_features=cat_idx))  # no eval_set for final fit
+
+    # SHAP on full data for reference
+    shap_full = shap_values_mean_abs(final_model, X, categorical_cols)
+
+    return {
+        "cv_metrics": cv_metrics,
+        "cv_metrics_mean": cv_metrics_mean,
+        "best_iterations": best_iters,
+        "shap_cv_mean": shap_cv_mean,
+        "model_full": final_model,
+        "shap_full": shap_full,
+    }
+
+
+# -------------------------------
+# Plotting helpers
+# -------------------------------
+def _normalize(s):
+    tot = float(s.sum())
+    return s / tot if tot > 0 else s
+
+
+def plot_bar_importance(
+    series,
+    title,
+    outfile=None,
+    normalized=True,
+    figsize=(6, 4),
+    rotate=0,
+    color="#4C78A8",
+):
+    """
+    If outfile ends with .csv/.json/.parquet, save the importance data to that file (no plot).
+    Otherwise, create a bar plot and save/show as before.
+    """
+    s = series.copy()
+    if normalized:
+        s = _normalize(s)
+
+    ext = str(outfile).lower() if outfile is not None else ""
+    is_csv = ext.endswith(".csv")
+    is_json = ext.endswith(".json")
+    is_parquet = ext.endswith(".parquet")
+
+    if outfile is not None and (is_csv or is_json or is_parquet):
+        df = s.rename_axis("Feature").reset_index(name="Importance")
+        Path(outfile).parent.mkdir(parents=True, exist_ok=True)
+        print(f"Saving feature importance data to: {Path(outfile).resolve()}")
+        if is_csv:
+            df.to_csv(outfile, index=False)
+        elif is_json:
+            df.to_json(outfile, orient="records")
+        elif is_parquet:
+            df.to_parquet(outfile, index=False)
+        return df
+
+    plt.figure(figsize=figsize)
+    plt.bar(s.index, s.values, color=color)
+    plt.ylabel(
+        "SHAP importance" + (" (normalized)" if normalized else " (mean |SHAP|)")
+    )
+    plt.title(title)
+    if rotate:
+        plt.xticks(rotation=rotate, ha="right")
+    plt.tight_layout()
+    if outfile is not None:
+        Path(outfile).parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(outfile, dpi=200)
+        plt.close()
+    else:
+        plt.show()
+    return s
+
+
+# -------------------------------
+# Pipeline per scope/year/stat (SHAP only)
+# -------------------------------
+def run_ml_importance_for_scope(
+    cell_df,
+    scope="Global",
+    years=(2030, 2050, 2070, 2100),
+    target_stat="mean",
+    output_dir="ml_importance_plots",
+    cv_folds=5,
+    random_state=0,
+    model_params=None,
+    normalized_plots=True,
+    model_type="final",  # "final" or "cv-mean"
+):
+    """
+    For the given scope (Global or Regional cell-level data), fit CatBoost models per year with CV.
+    """
+    results = {}
+    target_name = f"Y_{target_stat.lower()}"
+    df = cell_df.copy()
+    df = df.rename(columns={"Y": target_name})
+
+    feature_cols = ["Optimization", "Regret", "Scenario", "Welfare"]
+    if target_stat.lower() == "raw":
+        feature_cols = feature_cols + ["Sample"]
+    categorical_cols = feature_cols[:]
+
+    outbase = Path(output_dir) / scope.lower() / target_stat.lower()
+    outbase.mkdir(parents=True, exist_ok=True)
+
+    print(f"Saving plots to: {outbase.resolve()}")
+
+    for yr in years:
+        d = df[df["Year"] == yr].copy()
+        if d.empty:
+            continue
+
+        year_results = {}
+
+        if scope == "Global":
+            X = d[feature_cols].copy()
+            y = d[target_name].copy()
+
+            res = fit_catboost_cv_shap(
+                X,
+                y,
+                categorical_cols,
+                target_stat=target_stat,
+                cv_folds=cv_folds,
+                random_state=random_state,
+                params=model_params,
+            )
+
+            year_results.update(res)
+
+            if model_type == "cv-mean":
+                plot_bar_importance(
+                    res["shap_cv_mean"],
+                    title=f"{scope} {yr} — SHAP importance (CV mean, {target_stat})",
+                    outfile=outbase / f"{scope.lower()}_{yr}_shap_cv.csv",
+                    normalized=normalized_plots,
+                    figsize=(6, 4),
+                )
+            elif model_type == "final":
+                plot_bar_importance(
+                    res["shap_full"],
+                    title=f"{scope} {yr} — SHAP importance (final model, {target_stat})",
+                    outfile=outbase / f"{scope.lower()}_{yr}_shap_full.csv",
+                    normalized=normalized_plots,
+                    figsize=(6, 4),
+                )
+
+        else:
+            year_results["regions"] = {}
+            for region, d_r in d.groupby("Region", observed=True):
+                X = d_r[feature_cols].copy()
+                y = d_r[target_name].copy()
+                if len(X) < 4:
+                    continue
+
+                res = fit_catboost_cv_shap(
+                    X,
+                    y,
+                    categorical_cols,
+                    target_stat=target_stat,
+                    cv_folds=cv_folds,
+                    random_state=random_state,
+                    params=model_params,
+                )
+                year_results["regions"][str(region)] = res
+
+                safe_region = str(region).replace(" ", "_").replace("/", "_")
+                if model_type == "cv-mean":
+                    plot_bar_importance(
+                        res["shap_cv_mean"],
+                        title=f"{region} {yr} — SHAP importance (CV mean, {target_stat})",
+                        outfile=outbase / f"{safe_region}_{yr}_shap_cv.csv",
+                        normalized=normalized_plots,
+                        figsize=(6, 4),
+                    )
+                elif model_type == "final":
+                    plot_bar_importance(
+                        res["shap_full"],
+                        title=f"{region} {yr} — SHAP importance (final model, {target_stat})",
+                        outfile=outbase / f"{safe_region}_{yr}_shap_full.csv",
+                        normalized=normalized_plots,
+                        figsize=(6, 4),
+                    )
+
+        results[yr] = year_results
+
+    return results
+
+
+# -------------------------------
+# Convenience runner for all stats and either scope (SHAP only)
+# -------------------------------
+def run_all_ml_importance(
+    long_df,
+    years=(2030, 2050, 2070, 2100),
+    target_stats=("mean", "median", "p90"),
+    output_dir="ml_importance_plots",
+    cv_folds=5,
+    random_state=0,
+    model_params=None,
+    normalized_plots=True,
+    model_type="final",  # "final" or "cv-mean"
+    scope="global",  # or "regional"
+):
+    """
+    Build cell-level targets for each requested statistic and run CatBoost + SHAP
+    for either Global or Regional scope.
+    """
+    all_results = {}
+    scope = scope.lower()
+    for stat in target_stats:
+        if scope == "regional":
+            regional_cells = build_cell_level_targets(
+                long_df, years=years, target_stat=stat, scope="Regional"
+            )
+            regional_results = run_ml_importance_for_scope(
+                regional_cells,
+                scope="Regional",
+                years=years,
+                target_stat=stat,
+                output_dir=output_dir,
+                cv_folds=cv_folds,
+                random_state=random_state,
+                model_params=model_params,
+                normalized_plots=normalized_plots,
+                model_type=model_type,
+            )
+            all_results[stat] = {"Regional": regional_results}
+        elif scope == "global":
+            global_cells = build_cell_level_targets(
+                long_df, years=years, target_stat=stat, scope="Global"
+            )
+            global_results = run_ml_importance_for_scope(
+                global_cells,
+                scope="Global",
+                years=years,
+                target_stat=stat,
+                output_dir=output_dir,
+                cv_folds=cv_folds,
+                random_state=random_state,
+                model_params=model_params,
+                normalized_plots=normalized_plots,
+                model_type=model_type,
+            )
+            all_results[stat] = {"Global": global_results}
+        else:
+            raise ValueError("scope must be either 'global' or 'regional'")
+    return all_results
diff --git a/justice/util/output_data_processor.py b/justice/util/output_data_processor.py
index 0866ad4..9a71c7a 100644
--- a/justice/util/output_data_processor.py
+++ b/justice/util/output_data_processor.py
@@ -114,6 +114,9 @@ def reevaluated_optimal_policy_variable_extractor(
                     + variable_name
                 )
 
+            # Check if output directory exists, if not create it
+            if not path_to_output.exists():
+                path_to_output.mkdir(parents=True, exist_ok=True)
             # Save it as npy file
             out_path = path_to_output / f"{output_file_name}.npy"
             np.save(out_path, processed_data)
@@ -141,6 +144,8 @@ def reevaluate_optimal_policy(
     max_difference=2.0,
     min_difference=0.0,
     model_hard_reset=False,
+    reference_scenario=None,
+    regret_type=None,
 ):
     """
     Function to generate data for the optimal policy. It runs JUSTICE on the optimal policy and saves the data as a pickle file.
@@ -223,7 +228,20 @@ def reevaluate_optimal_policy(
                     min_difference=min_difference,
                 )
 
-                output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
+                if reference_scenario is not None and regret_type is not None:
+                    print("Saving file as ", output_file_name)
+                    output_file_name = (
+                        output_file_name
+                        + "_ref_"
+                        + reference_scenario
+                        + "_"
+                        + regret_type
+                        + "_idx"
+                        + str(rbf_policy_index)
+                    )
+                else:
+                    print("Saving file as ", output_file_name)
+                    output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
 
                 # Now save in hdf5 format
                 with h5py.File(path_to_output + output_file_name + ".h5", "w") as f:
@@ -261,7 +279,21 @@ def reevaluate_optimal_policy(
                 max_difference=max_difference,
                 min_difference=min_difference,
             )
-            output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
+
+            if reference_scenario is not None and regret_type is not None:
+                print("Saving file as ", output_file_name)
+                output_file_name = (
+                    output_file_name
+                    + "_ref_"
+                    + reference_scenario
+                    + "_"
+                    + regret_type
+                    + "_idx"
+                    + str(rbf_policy_index)
+                )
+            else:
+                print("Saving file as ", output_file_name)
+                output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
 
             # Save as HDF5 file
             with h5py.File(path_to_output + output_file_name + ".h5", "w") as f:
@@ -299,7 +331,20 @@ def reevaluate_optimal_policy(
                 max_difference=max_difference,
                 min_difference=min_difference,
             )
-            output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
+            if reference_scenario is not None and regret_type is not None:
+                print("Saving file as ", output_file_name)
+                output_file_name = (
+                    output_file_name
+                    + "_ref_"
+                    + reference_scenario
+                    + "_"
+                    + regret_type
+                    + "_idx"
+                    + str(rbf_policy_index)
+                )
+            else:
+                print("Saving file as ", output_file_name)
+                output_file_name = output_file_name + "_idx" + str(rbf_policy_index)
 
             # Save as HDF5 file
             with h5py.File(path_to_output + output_file_name + ".h5", "w") as f:
@@ -1398,7 +1443,6 @@ def compute_p90_regret_dataframe(
         raise ValueError(
             "No valid baseline data found in mapping for baseline_scenario and variable_of_interest."
         )
-
     # Sort list by median value
     median_list.sort(key=lambda x: x[1])
 
diff --git a/justice/util/pareto_nash_run.py b/justice/util/pareto_nash_run.py
new file mode 100644
index 0000000..1f3585e
--- /dev/null
+++ b/justice/util/pareto_nash_run.py
@@ -0,0 +1,619 @@
+import os
+import json
+import csv
+import time
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from itertools import product
+import multiprocessing as mp
+from typing import Any, Dict, List, Tuple, Optional
+
+from solvers.emodps.rbf import RBF
+
+from justice.util.enumerations import (
+    Economy,
+    DamageFunction,
+    Abatement,
+    WelfareFunction,
+)
+from justice.model import JUSTICE
+from justice.util.emission_control_constraint import EmissionControlConstraint
+from justice.util.regional_configuration import (
+    aggregate_by_macro_region,
+    build_macro_region_mapping,
+)
+from justice.objectives.objective_functions import fraction_of_ensemble_above_threshold
+from justice.util.data_loader import DataLoader
+from justice.util.model_time import TimeHorizon
+
+
+# ------------------------ policy bank ------------------------
+
+
+def build_policy_bank_from_5row_csv(
+    policy_csv_path: str,
+    config_path: str,
+    n_agents: int = 5,
+) -> Tuple[np.ndarray, Dict]:
+    with open(config_path, "r", encoding="utf-8") as f:
+        config = json.load(f)
+
+    df = pd.read_csv(policy_csv_path)
+    if len(df) != 5:
+        raise ValueError(
+            f"Expected CSV to have exactly 5 rows (5 actions), got {len(df)}"
+        )
+
+    n_inputs = int(config["n_inputs"])
+    rbf_template = RBF(n_rbfs=(n_inputs + 2), n_inputs=n_inputs, n_outputs=1)
+    centers_shape, radii_shape, weights_shape = rbf_template.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+    decision_var_len = centers_len + radii_len + weights_len
+
+    policy_bank = np.empty((n_agents, 5, decision_var_len), dtype=np.float64)
+
+    for a in range(5):  # action index = row index
+        row = df.iloc[a]
+        for i in range(n_agents):
+            centers = np.array(
+                [row[f"center {i} {j}"] for j in range(centers_len)], dtype=np.float64
+            )
+            radii = np.array(
+                [row[f"radii {i} {j}"] for j in range(radii_len)], dtype=np.float64
+            )
+            weights = np.array(
+                [row[f"weights {i} {j}"] for j in range(weights_len)], dtype=np.float64
+            )
+            policy_bank[i, a, :] = np.concatenate([centers, radii, weights])
+
+    return policy_bank, config
+
+
+# ------------------------ multiprocessing worker ------------------------
+
+_G: Dict[str, Any] = {}
+
+
+def _init_worker(
+    policy_bank: np.ndarray, config: Dict, scenario: int, mapping_base_path: str
+):
+    # prevent BLAS oversubscription inside each process
+    os.environ.setdefault("OMP_NUM_THREADS", "1")
+    os.environ.setdefault("MKL_NUM_THREADS", "1")
+    os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
+
+    _G["policy_bank"] = policy_bank
+    _G["config"] = config
+    _G["scenario"] = scenario
+    _G["mapping_base_path"] = mapping_base_path
+
+    # --- config values used repeatedly ---
+    start_year = config["start_year"]
+    end_year = config["end_year"]
+    data_timestep = config["data_timestep"]
+    timestep = config["timestep"]
+    emission_control_start_year = config["emission_control_start_year"]
+
+    n_inputs = int(config["n_inputs"])
+    temperature_year_of_interest = config["temperature_year_of_interest"]
+    stochastic_run = config["stochastic_run"]
+    climate_members = config.get("climate_ensemble_members")
+
+    min_temperature = config["min_temperature"]
+    max_temperature = config["max_temperature"]
+    min_temperature_change = config["min_temperature_change"]
+    max_temperature_change = config["max_temperature_change"]
+    consumption_min = config["consumption_min"]
+    consumption_max = config["consumption_max"]
+
+    _G["n_inputs"] = n_inputs
+    _G["temperature_year_of_interest"] = temperature_year_of_interest
+    _G["min_temperature"] = min_temperature
+    _G["max_temperature"] = max_temperature
+    _G["min_temperature_change"] = min_temperature_change
+    _G["max_temperature_change"] = max_temperature_change
+    _G["consumption_min"] = consumption_min
+    _G["consumption_max"] = consumption_max
+
+    # --- mapping / regions (build once) ---
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+    n_regions = len(region_list)
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    emission_start_ts = time_horizon.year_to_timestep(
+        year=emission_control_start_year, timestep=timestep
+    )
+    temperature_year_index = time_horizon.year_to_timestep(
+        year=temperature_year_of_interest, timestep=timestep
+    )
+    n_timesteps = len(time_horizon.model_time_horizon)
+
+    r5_json = Path(mapping_base_path) / "R5_regions.json"
+    rice50_json = Path(mapping_base_path) / "rice50_regions_dict.json"
+    region_to_macro, macro_region_names = build_macro_region_mapping(
+        region_list=region_list,
+        r5_json_path=r5_json,
+        rice50_json_path=rice50_json,
+    )
+    n_macro = len(macro_region_names)
+    if n_macro != 5:
+        raise RuntimeError(f"Expected 5 macro regions, got {n_macro}")
+
+    _G["region_to_macro"] = region_to_macro
+    _G["n_regions"] = n_regions
+    _G["n_macro"] = n_macro
+    _G["n_timesteps"] = n_timesteps
+    _G["temperature_year_index"] = temperature_year_index
+    _G["emission_start_ts"] = emission_start_ts
+
+    # --- instantiate model ONCE per worker ---
+    model = JUSTICE(
+        scenario=scenario,
+        economy_type=Economy.NEOCLASSICAL,
+        damage_function_type=DamageFunction.KALKUHL,
+        abatement_type=Abatement.ENERDATA,
+        social_welfare_function=WelfareFunction.UTILITARIAN,
+        stochastic_run=stochastic_run,
+        climate_ensembles=climate_members,
+    )
+    _G["model"] = model
+    _G["no_of_ensembles"] = int(model.__getattribute__("no_of_ensembles"))
+
+    # --- constraint (once) ---
+    _G["emission_constraint"] = EmissionControlConstraint(
+        max_annual_growth_rate=0.04,
+        emission_control_start_timestep=emission_start_ts,
+        min_emission_control_rate=0.01,
+    )
+
+    # --- precompute constants & preallocate buffers (once) ---
+    inv_temperature_range = 1.0 / (max_temperature - min_temperature)
+    inv_temperature_change_range = 1.0 / (
+        max_temperature_change - min_temperature_change
+    )
+    inv_consumption_range = 1.0 / (consumption_max - consumption_min)
+
+    _G["inv_temperature_range"] = inv_temperature_range
+    _G["inv_temperature_change_range"] = inv_temperature_change_range
+    _G["inv_consumption_range"] = inv_consumption_range
+
+    noE = _G["no_of_ensembles"]
+    _G["rbf_in"] = np.empty((n_inputs, noE), dtype=np.float64)
+    _G["prev_temp"] = np.zeros(noE, dtype=np.float64)
+    _G["prev_dtemp"] = np.zeros(noE, dtype=np.float64)
+
+    _G["regional_ecr"] = np.zeros((n_regions, n_timesteps, noE), dtype=np.float64)
+    _G["constrained_ecr"] = np.zeros((n_regions, n_timesteps, noE), dtype=np.float64)
+    _G["macro_ecr"] = np.zeros((n_macro, n_timesteps, noE), dtype=np.float64)
+    _G["macro_cpc_hist"] = np.zeros((n_macro, n_timesteps, noE), dtype=np.float64)
+
+    # region population is fixed conditional on scenario/config; compute once
+    _G["region_population"] = model.economy.get_population()
+
+
+def _simulate_profile(
+    actions: Tuple[int, int, int, int, int],
+) -> Tuple[Tuple[int, int, int, int, int], np.ndarray, float]:
+    """
+    actions: (a0..a4) each in {0..4}
+    Returns: (actions, welfare_vec(5,), fraction_above_threshold)
+    """
+    policy_bank = _G["policy_bank"]
+    model: JUSTICE = _G["model"]
+
+    n_inputs = _G["n_inputs"]
+    region_to_macro = _G["region_to_macro"]
+    emission_constraint: EmissionControlConstraint = _G["emission_constraint"]
+
+    n_regions = _G["n_regions"]
+    n_macro = _G["n_macro"]
+    n_timesteps = _G["n_timesteps"]
+    noE = _G["no_of_ensembles"]
+
+    min_temperature = _G["min_temperature"]
+    min_temperature_change = _G["min_temperature_change"]
+    consumption_min = _G["consumption_min"]
+
+    inv_temperature_range = _G["inv_temperature_range"]
+    inv_temperature_change_range = _G["inv_temperature_change_range"]
+    inv_consumption_range = _G["inv_consumption_range"]
+
+    temperature_year_index = _G["temperature_year_index"]
+
+    # --- reset model state (key change) ---
+    model.reset()
+
+    # --- build RBFs for this profile (small overhead; could be further cached if needed) ---
+    macro_rbfs: List[RBF] = []
+    for i in range(n_macro):
+        rbf = RBF(n_rbfs=(n_inputs + 2), n_inputs=n_inputs, n_outputs=1)
+        rbf.set_decision_vars(policy_bank[i, actions[i], :])
+        macro_rbfs.append(rbf)
+
+    # --- reuse buffers ---
+    regional_ecr = _G["regional_ecr"]
+    regional_ecr.fill(0.0)
+    constrained_ecr = _G["constrained_ecr"]
+    constrained_ecr.fill(0.0)
+    macro_ecr = _G["macro_ecr"]
+    macro_ecr.fill(0.0)
+    macro_cpc_hist = _G["macro_cpc_hist"]
+    macro_cpc_hist.fill(0.0)
+
+    rbf_in = _G["rbf_in"]
+    prev_temp = _G["prev_temp"]
+    prev_temp.fill(0.0)
+    prev_dtemp = _G["prev_dtemp"]
+    prev_dtemp.fill(0.0)
+
+    region_population = _G["region_population"]
+
+    # --- run ---
+    for t in range(n_timesteps):
+        constrained_ecr[:, t, :] = emission_constraint.constrain_emission_control_rate(
+            regional_ecr[:, t, :], t, allow_fallback=False
+        )
+
+        model.stepwise_run(
+            emission_control_rate=constrained_ecr[:, t, :],
+            timestep=t,
+            endogenous_savings_rate=True,
+        )
+        ds_t = model.stepwise_evaluate(timestep=t)
+
+        temp = ds_t["global_temperature"][t, :]
+        cons = ds_t["consumption"][:, t, :]
+
+        if t == 0:
+            dtemp = np.zeros_like(temp)
+            prev_temp[:] = temp
+            prev_dtemp[:] = dtemp
+        elif t % 5 == 0:
+            dtemp = temp - prev_temp
+            prev_temp[:] = temp
+            prev_dtemp[:] = dtemp
+        else:
+            dtemp = prev_dtemp
+
+        rbf_in[0, :] = np.clip(
+            (temp - min_temperature) * inv_temperature_range, 0.0, 1.0
+        )
+        rbf_in[1, :] = np.clip(
+            (dtemp - min_temperature_change) * inv_temperature_change_range, 0.0, 1.0
+        )
+
+        pop_t = region_population[:, t, :]
+        macro_pop = aggregate_by_macro_region(pop_t, region_to_macro)
+        macro_total_cons = aggregate_by_macro_region(cons, region_to_macro)
+        macro_cpc = (macro_total_cons / macro_pop) * 1e3
+        macro_cpc_hist[:, t, :] = macro_cpc
+
+        norm_macro_cpc = np.clip(
+            (macro_cpc - consumption_min) * inv_consumption_range, 0.0, 1.0
+        )
+
+        if t < n_timesteps - 1:
+            for i, rbf in enumerate(macro_rbfs):
+                rbf_in[2, :] = norm_macro_cpc[i, :]
+                macro_ecr[i, t + 1, :] = rbf.apply_rbfs(rbf_in)
+            regional_ecr[:, t + 1, :] = macro_ecr[region_to_macro, t + 1, :]
+
+    ds = model.evaluate()
+
+    welfare_vec = model.welfare_function.calculate_spatially_disaggregated_welfare(
+        macro_cpc_hist
+    )
+    welfare_vec = np.asarray(welfare_vec, dtype=np.float64)
+
+    frac = fraction_of_ensemble_above_threshold(
+        temperature=ds["global_temperature"],
+        temperature_year_index=temperature_year_index,
+        threshold=2.0,
+    )
+
+    return actions, welfare_vec, float(frac)
+
+
+# ------------------------ benchmark + full run ------------------------
+
+
+def benchmark_profiles(
+    policy_csv_path: str,
+    config_path: str,
+    scenario: int = 2,
+    mapping_base_path: str = "data/input",
+    n_samples: int = 20,
+    seed: int = 0,
+) -> None:
+    """
+    Runs n_samples profiles sequentially in THIS process to estimate time/profile.
+    Uses the same init/reset logic as workers.
+    """
+    rng = np.random.default_rng(seed)
+    policy_bank, config = build_policy_bank_from_5row_csv(
+        policy_csv_path, config_path, n_agents=5
+    )
+
+    _init_worker(policy_bank, config, scenario, mapping_base_path)
+
+    samples = [tuple(rng.integers(0, 5, size=5).tolist()) for _ in range(n_samples)]
+
+    t0 = time.perf_counter()
+    for a in samples:
+        _simulate_profile(a)
+    t1 = time.perf_counter()
+
+    per = (t1 - t0) / n_samples
+    est_total = per * (5**5)
+    print(f"Benchmark: {n_samples} samples in {t1-t0:.3f}s")
+    print(f"Time/profile: {per:.4f}s")
+    print(f"Estimated time for 5^5=3125 profiles: {est_total/60:.2f} minutes")
+
+
+def run_5pow5_payoff_table(
+    policy_csv_path: str,
+    config_path: str,
+    out_csv_path: str,
+    scenario: int = 2,
+    mapping_base_path: str = "data/input",
+    n_workers: Optional[int] = None,
+    chunksize: int = 8,
+) -> None:
+    policy_bank, config = build_policy_bank_from_5row_csv(
+        policy_csv_path, config_path, n_agents=5
+    )
+
+    profiles = list(product(range(5), repeat=5))  # 3125
+
+    out_path = Path(out_csv_path)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    header = [
+        "a0",
+        "a1",
+        "a2",
+        "a3",
+        "a4",
+        "welfare_0",
+        "welfare_1",
+        "welfare_2",
+        "welfare_3",
+        "welfare_4",
+        "fraction_above_threshold",
+    ]
+
+    # If you are on Linux and want faster startup, you can try "fork".
+    # "spawn" is safest cross-platform.
+    ctx = mp.get_context("spawn")
+
+    if n_workers is None:
+        n_workers = max(1, mp.cpu_count() - 1)
+
+    t0 = time.perf_counter()
+    completed = 0
+    report_every = 100
+
+    with out_path.open("w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(header)
+
+        with ctx.Pool(
+            processes=n_workers,
+            initializer=_init_worker,
+            initargs=(policy_bank, config, scenario, mapping_base_path),
+        ) as pool:
+            for actions, welfare_vec, frac in pool.imap_unordered(
+                _simulate_profile, profiles, chunksize=chunksize
+            ):
+                writer.writerow([*actions, *welfare_vec.tolist(), frac])
+                completed += 1
+
+                if completed % report_every == 0:
+                    t = time.perf_counter() - t0
+                    rate = completed / max(t, 1e-9)
+                    eta = (len(profiles) - completed) / max(rate, 1e-9)
+                    print(
+                        f"{completed}/{len(profiles)} done | {rate:.2f} проф/s | ETA {eta/60:.1f} min"
+                    )
+
+    t1 = time.perf_counter()
+    print(f"Finished {len(profiles)} profiles in {(t1-t0)/60:.2f} minutes")
+
+
+def nondominated_mask(points: np.ndarray) -> np.ndarray:
+    """
+    points: (m, k) array, all objectives assumed to be MAXIMIZED.
+    Returns mask of rows that are NOT Pareto-dominated by any other row.
+    """
+    m = points.shape[0]
+    dominated = np.zeros(m, dtype=bool)
+
+    # O(m^2 * k) but here groups are tiny (e.g., 5 actions), so it's fast.
+    for i in range(m):
+        if dominated[i]:
+            continue
+        ge = (points >= points[i]).all(axis=1)
+        gt = (points > points[i]).any(axis=1)
+        if np.any(ge & gt):
+            dominated[i] = True
+    return ~dominated
+
+
+def infer_agent_count(
+    df: pd.DataFrame,
+    welfare_prefix: str = "welfare_",
+    action_prefix: str = "a",
+) -> int:
+    """
+    Infers n_agents from columns welfare_0..welfare_{n-1} or a0..a{n-1}.
+    """
+    welfare_idx = []
+    for c in df.columns:
+        if c.startswith(welfare_prefix):
+            suf = c[len(welfare_prefix) :]
+            if suf.isdigit():
+                welfare_idx.append(int(suf))
+
+    action_idx = []
+    for c in df.columns:
+        if c.startswith(action_prefix):
+            suf = c[len(action_prefix) :]
+            if suf.isdigit():
+                action_idx.append(int(suf))
+
+    candidates = []
+    if welfare_idx:
+        candidates.append(max(welfare_idx) + 1)
+    if action_idx:
+        candidates.append(max(action_idx) + 1)
+
+    if not candidates:
+        raise ValueError(
+            "Could not infer number of agents from columns (no welfare_* or a* columns found)."
+        )
+
+    n_agents = max(candidates)
+    return n_agents
+
+
+def pareto_nash_set(
+    payoff_csv_path: str,
+    fraction_col: str = "fraction_above_threshold",
+    minimize_fraction: bool = True,
+    welfare_prefix: str = "welfare_",
+    action_prefix: str = "a",
+    n_agents: Optional[int] = None,
+) -> pd.DataFrame:
+    """
+    Computes the Pareto–Nash set for an n-player game where each player's outcome is 2D:
+      [welfare_i (maximize), -fraction (maximize)]  if minimize_fraction=True
+
+    Returns a dataframe subset with boolean columns br_i and is_pareto_nash.
+
+    Notes
+    -----
+    Requires action columns a0..a{n-1} and welfare columns welfare_0..welfare_{n-1}.
+    """
+    df = pd.read_csv(payoff_csv_path)
+
+    if n_agents is None:
+        n_agents = infer_agent_count(
+            df, welfare_prefix=welfare_prefix, action_prefix=action_prefix
+        )
+
+    action_cols = [f"{action_prefix}{i}" for i in range(n_agents)]
+    welfare_cols = [f"{welfare_prefix}{i}" for i in range(n_agents)]
+
+    missing = [
+        c for c in action_cols + welfare_cols + [fraction_col] if c not in df.columns
+    ]
+    if missing:
+        raise ValueError(f"Missing required columns: {missing}")
+
+    actions_arr = df[action_cols].to_numpy(dtype=np.int64)
+    welfare_arr = df[welfare_cols].to_numpy(dtype=np.float64)
+
+    frac_raw = df[fraction_col].to_numpy(dtype=np.float64)
+    frac_obj = (
+        -frac_raw if minimize_fraction else frac_raw
+    )  # convert to maximize objective
+
+    br_masks: List[np.ndarray] = []
+
+    for i in range(n_agents):
+        others = [j for j in range(n_agents) if j != i]
+        keys = actions_arr[:, others]  # (N, n_agents-1)
+
+        # Fast grouping by "others": turn each key row into bytes
+        keys_struct = np.ascontiguousarray(keys).view(
+            np.dtype((np.void, keys.dtype.itemsize * keys.shape[1]))
+        )
+        _, group_id = np.unique(keys_struct, return_inverse=True)
+
+        br = np.zeros(len(df), dtype=bool)
+
+        # iterate groups; each group contains all unilateral actions for player i
+        # (typically size = number of actions per player)
+        for gid in np.unique(group_id):
+            idx = np.where(group_id == gid)[0]
+            pts = np.column_stack([welfare_arr[idx, i], frac_obj[idx]])
+            br[idx] = nondominated_mask(pts)
+
+        df[f"br_{i}"] = br
+        br_masks.append(br)
+
+    df["is_pareto_nash"] = np.logical_and.reduce(br_masks)
+    return df[df["is_pareto_nash"]].copy()
+
+
+def main():
+    policy_csv_path = (
+        "data/temporary/MOMA_DATA/200k/COMBINED_MOMA_epsilon_nondominated_set.csv"
+    )
+    config_path = "analysis/momadps_config.json"
+    mapping_base_path = "data/input"
+
+    # 1) quick sequential benchmark (no multiprocessing)
+    benchmark_profiles(
+        policy_csv_path=policy_csv_path,
+        config_path=config_path,
+        scenario=2,
+        mapping_base_path=mapping_base_path,
+        n_samples=10,
+        seed=0,
+    )
+
+    # 2) full parallel run (5^5 profiles)
+    out_csv_path = "data/temporary/MOMA_DATA/200k/out/payoff_table_5x5x5x5x5.csv"
+    Path(out_csv_path).parent.mkdir(parents=True, exist_ok=True)
+
+    run_5pow5_payoff_table(
+        policy_csv_path=policy_csv_path,
+        config_path=config_path,
+        out_csv_path=out_csv_path,
+        scenario=2,
+        mapping_base_path=mapping_base_path,
+        n_workers=4,  # tune
+        chunksize=8,  # tune
+    )
+
+
+if __name__ == "__main__":
+    # # Required on macOS/Windows when using "spawn"
+    # mp.freeze_support()
+    # # Optional: be explicit (recommended for reproducibility)
+    # mp.set_start_method("spawn", force=True)
+
+    # main()
+    payoff_csv = "data/temporary/MOMA_DATA/200k/out/payoff_table_5x5x5x5x5.csv"
+
+    pn = pareto_nash_set(
+        payoff_csv_path=payoff_csv,
+        minimize_fraction=True,  # usually you want to minimize fraction above threshold
+        fraction_col="fraction_above_threshold",
+        welfare_prefix="welfare_",
+        action_prefix="a",
+        n_agents=None,  # infer from columns
+    )
+
+    print("Pareto–Nash profiles:", len(pn))
+    cols_to_show = [
+        c for c in pn.columns if c.startswith("a") or c.startswith("welfare_")
+    ] + ["fraction_above_threshold"]
+    print(pn[cols_to_show].head())
+
+    # Save Pareto–Nash profiles to CSV
+    pn_out_path = "data/temporary/MOMA_DATA/200k/out/pareto_nash_profiles.csv"
+    pn.to_csv(pn_out_path, index=False)
+    print(f"Saved Pareto–Nash profiles to {pn_out_path}")
diff --git a/justice/util/postprocessing_for_regret_calculations.py b/justice/util/postprocessing_for_regret_calculations.py
new file mode 100644
index 0000000..b9c1fac
--- /dev/null
+++ b/justice/util/postprocessing_for_regret_calculations.py
@@ -0,0 +1,111 @@
+# export PYTHONPATH=$PYTHONPATH:/Users/palokbiswas/Desktop/pollockdevis_git/JUSTICE/
+from justice.util.output_data_processor import process_scenario
+
+import os
+import sys
+import filecmp
+import pandas as pd
+from pathlib import Path
+import multiprocessing as mp
+from functools import partial
+from justice.util.enumerations import WelfareFunction, SSP
+from justice.util.output_data_processor import (
+    process_scenario,
+    generate_reference_set_policy_mapping,
+    read_reference_set_policy_mapping,
+)
+
+from justice.util.output_data_processor import (
+    reevaluate_optimal_policy,
+    reevaluated_optimal_policy_variable_extractor,
+)
+from justice.util.model_time import TimeHorizon
+from justice.util.data_loader import DataLoader
+
+
+def process_scenario_parallel(
+    start_year,
+    end_year,
+    data_timestep,
+    timestep,
+    scenario_list,
+    social_welfare_function,
+    ssp,
+    base_path,
+):
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    list_of_years = time_horizon.model_time_horizon
+
+    sw_name = social_welfare_function.value[1]
+    ssp_name = ssp.name  # str(ssp).split(".")[1]
+    path = base_path + sw_name + "_" + ssp_name + "/"
+    # path = os.path.join(base_path, ssp_name, "")
+    filename = f"{sw_name}_reference_set.csv"
+    csv_path = os.path.join(path, filename)
+
+    loaded_df = pd.read_csv(csv_path)
+    policy_indices = list(range(len(loaded_df)))
+
+    print(f"Loading data for {sw_name} from {csv_path}")
+    print("Selected policy-indices last 2 columns:")
+    print(loaded_df.iloc[policy_indices, -2:])
+
+    try:
+        mp.set_start_method("spawn")
+    except RuntimeError:
+        pass
+
+    bound_process_scenario = partial(
+        process_scenario, social_welfare_function, path, policy_indices
+    )
+    with mp.Pool(processes=len(scenario_list)) as pool:
+        pool.map(bound_process_scenario, scenario_list)
+
+
+if __name__ == "__main__":
+
+    # Get  swf, ssp, base_dir from sys.argv or set default values
+    base_dir = sys.argv[1] if len(sys.argv) > 4 else "data/temporary/NU_DATA/mmBorg/"
+    swf_input = (sys.argv[2] if len(sys.argv) > 2 else "PRIORITARIAN").upper()
+    ssp_name = (sys.argv[3] if len(sys.argv) > 3 else "SSP1").upper()
+
+    ssp = SSP[ssp_name]
+
+    social_welfare_function = WelfareFunction[swf_input]
+
+    scenario_list = ["SSP126", "SSP245", "SSP370", "SSP460", "SSP534"]
+
+    print(f"Selected Social Welfare Function: {social_welfare_function}, SSP: {ssp}")
+
+    ########################################
+
+    # Step 1: Parallel Scenario Processing Block which produces welfare and temperature data for all policies and scenarios and ensemble members
+    process_scenario_parallel(
+        start_year=2015,
+        end_year=2300,
+        data_timestep=5,
+        timestep=1,
+        scenario_list=scenario_list,
+        social_welfare_function=social_welfare_function,
+        ssp=ssp,
+        base_path=base_dir,
+    )
+    ########################################
+    # Step 2: Generate Mapping from policy index to objective values
+    mapping = generate_reference_set_policy_mapping(
+        swf=social_welfare_function,
+        data_root=Path(base_dir + social_welfare_function.value[1] + "_" + ssp.name),
+        scenario_list=scenario_list,
+        saving=True,
+        output_directory="mapping",
+        delete_loaded_files=True,  # Set to True to delete the loaded files after processing
+    )
+    ########################################
diff --git a/justice/util/reevaluate_optimal_policy.py b/justice/util/reevaluate_optimal_policy.py
new file mode 100644
index 0000000..0bf32e9
--- /dev/null
+++ b/justice/util/reevaluate_optimal_policy.py
@@ -0,0 +1,119 @@
+import pandas as pd
+from justice.util.output_data_processor import (
+    reevaluate_optimal_policy,
+    reevaluated_optimal_policy_variable_extractor,
+)
+from justice.util.model_time import TimeHorizon
+from justice.util.data_loader import DataLoader
+from justice.util.enumerations import WelfareFunction, SSP
+import json
+import os
+
+
+def reevaluate_policies_for_all_scenarios(
+    base_path="data/temporary/NU_DATA/mmBorg/",
+    scenario_list=["SSP126", "SSP245", "SSP370", "SSP460", "SSP534"],
+    policy_indices_dict_name="min_regret_policy_indices.json",
+    output_path=None,
+    start_year=2015,
+    end_year=2300,
+    data_timestep=5,
+    timestep=1,
+):
+
+    # Throw error if output_path is None
+    if output_path is None:
+        raise ValueError(
+            "output_path cannot be None. Please provide a valid output path."
+        )
+
+    start_year = 2015
+    end_year = 2300
+    data_timestep = 5
+    timestep = 1
+
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+
+    list_of_years = time_horizon.model_time_horizon
+
+    with open(base_path + policy_indices_dict_name, "r") as f:
+        loaded_min_regret_policy_indices = json.load(f)
+
+    for scenario, ethical_data in loaded_min_regret_policy_indices.items():
+
+        for ethical_framing, regret_data in ethical_data.items():
+
+            for regret_type, policy_index in regret_data.items():
+
+                # ssp = SSP[scenario]
+                social_welfare_function = WelfareFunction[ethical_framing]
+                path = f"{base_path}/{ethical_framing}_{scenario}/"
+                # Print
+                print(
+                    f"Reevaluating for: {ethical_framing}, ref scenario: {scenario}, Regret {regret_type} Policy Index: {policy_index}"
+                )
+                # TODO: Temporarily commenting out reevaluation to just extract variables
+                reevaluate_optimal_policy(
+                    input_data=[
+                        f"{social_welfare_function.value[1]}_reference_set.csv",
+                    ],
+                    path_to_rbf_weights=path,  #  reevaluation
+                    path_to_output=output_path,  #  reevaluation
+                    direction_of_optimization=[
+                        "min",
+                        "min",
+                    ],
+                    rbf_policy_index=policy_index,  # selected_indices[0], # This chooses policy for a particular rival framing. Can also set to the index directly
+                    list_of_objectives=[
+                        "welfare",
+                        "fraction_above_threshold",
+                    ],
+                    scenario_list=scenario_list,  # [scenario], # This is only for a single scenario
+                    reference_scenario=scenario,
+                    regret_type=regret_type,
+                )
+
+                # ############################################################################################################
+                variable_names_and_shapes = {
+                    "global_temperature": 2,
+                    "constrained_emission_control_rate": 3,
+                    "emissions": 3,
+                }
+                input_data_name = f"{ethical_framing}_reference_set_ref_{scenario}_{regret_type}_idx{policy_index}.h5"
+                for variable_name, data_shape in variable_names_and_shapes.items():
+                    reevaluated_optimal_policy_variable_extractor(
+                        scenario_list=scenario_list,  # [scenario], # This is only for a single scenario
+                        region_list=region_list,
+                        list_of_years=list_of_years,
+                        path_to_data=base_path,
+                        path_to_output=base_path + f"/{ethical_framing}_{scenario}"
+                        f"/ref_{scenario}_{regret_type}_idx{policy_index}",  # TODO Change this later
+                        variable_name=variable_name,
+                        data_shape=data_shape,  # 2 for temperature, 3 for rest
+                        no_of_ensembles=1001,
+                        input_data=[
+                            input_data_name,
+                        ],
+                        output_file_names=[
+                            f"{ethical_framing}_ref_{scenario}_{regret_type}_idx{policy_index}_{variable_name}",
+                        ],
+                    )
+
+                # Delete the hdf5 file
+                os.remove(base_path + "/" + input_data_name)
+                print(f"Deleted HDF5 file at location {input_data_name}")
+
+
+if __name__ == "__main__":
+
+    reevaluate_policies_for_all_scenarios(
+        output_path="data/temporary/NU_DATA/mmBorg/"  # "/Volumes/justicedrive/NU_data_20_Oct/reevaluation/"
+    )
diff --git a/justice/util/regional_configuration.py b/justice/util/regional_configuration.py
index 2fac37a..160c675 100644
--- a/justice/util/regional_configuration.py
+++ b/justice/util/regional_configuration.py
@@ -10,9 +10,10 @@
 
 from collections import defaultdict
 import json
-import pycountry  # TODO Use this to get country names for ISO3 codes
 import pandas as pd
 import numpy as np
+from pathlib import Path
+from typing import Sequence
 
 
 def get_region_mapping(
@@ -113,3 +114,77 @@ def justice_region_aggregator(
         aggregated_data[index, :, :] = np.sum(data[indices, :, :], axis=0)
 
     return aggregated_region_list, aggregated_data
+
+
+def build_macro_region_mapping(
+    region_list: Sequence[str],
+    r5_json_path: Path,
+    rice50_json_path: Path,
+) -> tuple[np.ndarray, tuple[str, ...]]:
+    """
+    Build the `region_to_macro` index array and the ordered macro-region names.
+
+    Parameters
+    ----------
+    region_list:
+        Ordered list (or array) of 57 region identifiers corresponding to the
+        rows in `emis_control`.
+    r5_json_path:
+        Path to `R5_regions.json`.
+    rice50_json_path:
+        Path to `rice50_regions_dict.json`.
+
+    Returns
+    -------
+    region_to_macro:
+        Array of shape (57,) mapping each row index to a macro-region index.
+    macro_region_names:
+        Tuple of macro-region names in the same order as the aggregated axis.
+    """
+    with open(r5_json_path) as f:
+        macro_region_def = json.load(f)
+
+    with open(rice50_json_path) as f:
+        region_to_countries = json.load(f)
+
+    # Fast lookup from country code -> macro-region index
+    macro_region_names = tuple(macro_region_def.keys())
+    macro_code_to_index = {
+        code: idx
+        for idx, macro_name in enumerate(macro_region_names)
+        for code in macro_region_def[macro_name]
+    }
+
+    region_to_index = {region: idx for idx, region in enumerate(region_list)}
+    region_to_macro = np.full(len(region_list), -1, dtype=np.intp)
+
+    for region_name, country_codes in region_to_countries.items():
+        region_idx = region_to_index.get(region_name)
+        if region_idx is None:
+            continue  # or raise if this should never happen
+
+        for code in country_codes:
+            macro_idx = macro_code_to_index.get(code)
+            if macro_idx is not None:
+                region_to_macro[region_idx] = macro_idx
+                break
+
+    if (region_to_macro < 0).any():
+        missing = np.where(region_to_macro < 0)[0]
+        raise ValueError(
+            "Some regions lack macro-region assignment: "
+            f"{missing.tolist()} – verify your JSON files."
+        )
+
+    return region_to_macro, macro_region_names
+
+
+def aggregate_by_macro_region(
+    data: np.ndarray,
+    region_to_macro: np.ndarray,
+) -> np.ndarray:
+    aggregated = np.zeros(
+        (region_to_macro.max() + 1,) + data.shape[1:], dtype=data.dtype
+    )
+    np.add.at(aggregated, region_to_macro, data)
+    return aggregated
diff --git a/justice/util/visualizer.py b/justice/util/visualizer.py
index 9c6d23b..f0f59aa 100644
--- a/justice/util/visualizer.py
+++ b/justice/util/visualizer.py
@@ -20,11 +20,734 @@
 from justice.util.data_loader import DataLoader
 from plotly.subplots import make_subplots
 from sklearn.preprocessing import MinMaxScaler
-
 import json
 import pycountry
 import plotly.express as px
 import plotly.graph_objects as go
+import re
+from typing import Dict, List
+from typing import Iterable
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+import matplotlib.colors as mcolors
+import matplotlib.cm as cm
+from matplotlib.colors import LinearSegmentedColormap
+
+import plotly.colors as pc
+from matplotlib.patches import PathPatch
+import matplotlib.path as mpath
+
+# =============================================================================
+
+
+def load_regional_uncertainty_shares(
+    base_dir: str,
+    stat: str = "raw",
+    model_type: str = "final",
+    years: Iterable[int] = (2030, 2050, 2070, 2100),
+    FEATURE_ORDER=["Scenario", "Regret", "Welfare", "Optimization", "Sample"],
+) -> pd.DataFrame:
+    base = Path(base_dir) / "regional" / stat.lower()
+    if not base.exists():
+        raise FileNotFoundError(f"Directory not found: {base}")
+
+    kind = "shap_full" if model_type == "final" else "shap_cv"
+    pattern = re.compile(rf"^(?P<region>.+)_(?P<year>\d{{4}})_{kind}\.csv$")
+
+    records = []
+    for csv_path in base.glob("*.csv"):
+        m = pattern.match(csv_path.name)
+        if not m:
+            continue
+
+        region_slug = m.group("region")
+        year = int(m.group("year"))
+        if year not in years:
+            continue
+
+        df = pd.read_csv(csv_path)
+        if not {"Feature", "Importance"}.issubset(df.columns):
+            print(f"[warn] '{csv_path}' missing required columns. Skipping.")
+            continue
+
+        s = df.set_index("Feature")["Importance"]
+        s = s.reindex(FEATURE_ORDER, fill_value=0.0)
+        total = s.sum()
+        if total <= 0:
+            continue
+
+        shares = s / total
+        records.append(
+            {
+                "Region": region_slug,
+                "Year": year,
+                "Normative": float(shares[["Regret", "Welfare", "Optimization"]].sum()),
+                "Deep": float(shares["Scenario"]),
+                "Stochastic": float(shares["Sample"]),
+                "Scenario": float(shares["Scenario"]),
+                "Regret": float(shares["Regret"]),
+                "Welfare": float(shares["Welfare"]),
+                "Optimization": float(shares["Optimization"]),
+                "Sample": float(shares["Sample"]),
+            }
+        )
+
+    shares_df = pd.DataFrame(records)
+    if shares_df.empty:
+        raise ValueError("No valid regional CSVs found. Check paths/stat/model_type.")
+    return shares_df
+
+
+# =============================================================================
+# 2. Ternary background + color mixing
+# =============================================================================
+def mix_color(
+    normative,
+    deep,
+    stochastic,
+    base_colors=np.array(
+        [
+            [1.0, 0.0, 1.0],  # Normative → magenta
+            [1.0, 1.0, 0.0],  # Deep      → yellow
+            [0.0, 1.0, 1.0],  # Stochastic→ cyan
+        ]
+    ),
+    as_hex=True,
+):
+    weights = np.array([normative, deep, stochastic], dtype=float)
+    total = weights.sum()
+    if total <= 0:
+        raise ValueError("Normative + Deep + Stochastic must be positive.")
+    weights /= total
+    rgb = np.clip(weights @ base_colors, 0, 1)
+    return mcolors.to_hex(rgb) if as_hex else rgb
+
+
+def quantize_simplex(n, d, s, scale=8):
+    """Snap (n, d, s) onto a discrete ternary lattice with step size 1/scale."""
+    weights = np.array([n, d, s], dtype=float)
+    total = weights.sum()
+    if total <= 0:
+        raise ValueError("Normative + Deep + Stochastic must be positive.")
+    weights /= total
+
+    scaled = weights * scale
+    base = np.floor(scaled)
+    remainder = scale - int(base.sum())
+
+    if remainder > 0:
+        frac = scaled - base
+        for idx in np.argsort(-frac):
+            if remainder == 0:
+                break
+            base[idx] += 1
+            remainder -= 1
+    elif remainder < 0:
+        frac = scaled - base
+        for idx in np.argsort(frac):
+            if remainder == 0:
+                break
+            base[idx] -= 1
+            remainder += 1
+
+    snapped = base / scale
+    snapped /= snapped.sum()
+    return snapped
+
+
+def barycentric_to_cartesian(normative, deep, stochastic):
+    x = stochastic + 0.5 * normative
+    y = (np.sqrt(3) / 2.0) * normative
+    return x, y
+
+
+def build_triangular_mesh(scale):
+    bary_coords = []
+    cart_coords = []
+    idx_lookup = {}
+    idx = 0
+    for i in range(scale + 1):
+        for j in range(scale + 1 - i):
+            k = scale - i - j
+            n = i / scale
+            d = j / scale
+            s = k / scale
+            bary_coords.append(np.array([n, d, s]))
+            cart_coords.append(barycentric_to_cartesian(n, d, s))
+            idx_lookup[(i, j)] = idx
+            idx += 1
+
+    triangles = []
+    for i in range(scale):
+        for j in range(scale - i):
+            p0 = idx_lookup[(i, j)]
+            p1 = idx_lookup[(i + 1, j)]
+            p2 = idx_lookup[(i, j + 1)]
+            triangles.append((p0, p1, p2))
+            if i + j < scale - 1:
+                p3 = idx_lookup[(i + 1, j + 1)]
+                triangles.append((p1, p3, p2))
+    return bary_coords, cart_coords, triangles
+
+
+def draw_ternary_background(
+    scale=8,
+    base_colors=np.array(
+        [
+            [1.0, 0.0, 1.0],  # Normative → magenta
+            [1.0, 1.0, 0.0],  # Deep      → yellow
+            [0.0, 1.0, 1.0],  # Stochastic→ cyan
+        ]
+    ),
+    ax=None,
+    annotate=False,
+):
+    bary_coords, cart_coords, triangles = build_triangular_mesh(scale)
+
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(6.5, 6))
+    else:
+        fig = ax.figure
+
+    patches = []
+    colors = []
+    for tri in triangles:
+        verts = [cart_coords[idx] for idx in tri]
+        centroid = np.mean([bary_coords[idx] for idx in tri], axis=0)
+        face_color = mix_color(*centroid, base_colors=base_colors, as_hex=False)
+        patches.append(Polygon(verts))
+        colors.append(face_color)
+
+        if annotate:
+            cx, cy = barycentric_to_cartesian(*centroid)
+            ax.text(
+                cx,
+                cy,
+                f"{centroid[0]:.2f}\n{centroid[1]:.2f}\n{centroid[2]:.2f}",
+                ha="center",
+                va="center",
+                fontsize=6,
+                color="black",
+            )
+
+    pcoll = PatchCollection(patches, facecolors=colors, edgecolors="k", linewidths=0.3)
+    ax.add_collection(pcoll)
+
+    boundary = np.array(
+        [
+            barycentric_to_cartesian(0, 0, 1),
+            barycentric_to_cartesian(0, 1, 0),
+            barycentric_to_cartesian(1, 0, 0),
+            barycentric_to_cartesian(0, 0, 1),
+        ]
+    )
+    ax.plot(boundary[:, 0], boundary[:, 1], color="black", linewidth=1.25)
+
+    for i in range(1, scale):
+        t = i / scale
+        p1 = barycentric_to_cartesian(t, 0, 1 - t)
+        p2 = barycentric_to_cartesian(t, 1 - t, 0)
+        ax.plot([p1[0], p2[0]], [p1[1], p2[1]], color="white", alpha=0.6, linewidth=0.8)
+        p1 = barycentric_to_cartesian(0, t, 1 - t)
+        p2 = barycentric_to_cartesian(1 - t, t, 0)
+        ax.plot([p1[0], p2[0]], [p1[1], p2[1]], color="white", alpha=0.6, linewidth=0.8)
+        p1 = barycentric_to_cartesian(0, 1 - t, t)
+        p2 = barycentric_to_cartesian(1 - t, 0, t)
+        ax.plot([p1[0], p2[0]], [p1[1], p2[1]], color="white", alpha=0.6, linewidth=0.8)
+
+    ax.text(
+        0.5, np.sqrt(3) / 2 + 0.04, "Normative", ha="center", va="bottom", fontsize=12
+    )
+    ax.text(-0.04, -0.03, "Deep", ha="right", va="top", fontsize=12)
+    ax.text(1.04, -0.03, "Stochastic", ha="left", va="top", fontsize=12)
+
+    ax.set_xlim(-0.05, 1.05)
+    ax.set_ylim(-0.05, np.sqrt(3) / 2 + 0.08)
+    ax.set_aspect("equal")
+    ax.axis("off")
+    return fig, ax
+
+
+def add_regions_to_ternary(
+    ax,
+    df_year: pd.DataFrame,
+    scale: int = 8,
+    quantize: bool = True,
+    annotate: bool = False,
+    marker_size: float = 18,
+    jitter_strength: float = 0.02,
+    random_state: int = 0,
+):
+    rng = np.random.default_rng(random_state) if jitter_strength > 0 else None
+
+    for _, row in df_year.iterrows():
+        n, d, s = row["Normative"], row["Deep"], row["Stochastic"]
+        if quantize:
+            n, d, s = quantize_simplex(n, d, s, scale=scale)
+
+        weights = np.array([n, d, s], dtype=float)
+        if jitter_strength > 0:
+            noise = rng.normal(scale=jitter_strength, size=3)
+            weights = np.clip(weights + noise, 1e-6, None)
+            weights /= weights.sum()
+
+        x, y = barycentric_to_cartesian(*weights)
+        ax.scatter(
+            x,
+            y,
+            s=marker_size,
+            marker="X",
+            color="black",
+            edgecolor="none",
+        )
+
+        if annotate:
+            label = row["Region"].replace("_", " ")
+            ax.text(
+                x, y, label, fontsize=4, ha="left", va="top"
+            )  # ha and va mean horizontalalignment and verticalalignment
+
+
+# =============================================================================
+# 3. Choropleth helpers
+# =============================================================================
+def hex_to_rgb(hex_color: str) -> str:
+    hex_color = hex_color.lstrip("#")
+    r = int(hex_color[0:2], 16)
+    g = int(hex_color[2:4], 16)
+    b = int(hex_color[4:6], 16)
+    return f"rgb({r},{g},{b})"
+
+
+def build_choropleth(
+    df_year: pd.DataFrame,
+    region_to_iso_path: str,
+    scale: int = 8,
+    quantize: bool = True,
+    projection_type: str = "equal earth",
+) -> go.Figure:
+    with open(region_to_iso_path, "r", encoding="utf-8") as f:
+        region_to_iso = json.load(f)
+
+    rows = []
+    missing_regions = []
+
+    for _, row in df_year.iterrows():
+        label = row["Region"].replace("_", " ")
+        if label not in region_to_iso:
+            missing_regions.append(label)
+            continue
+
+        n, d, s = row["Normative"], row["Deep"], row["Stochastic"]
+        if quantize:
+            n, d, s = quantize_simplex(n, d, s, scale=scale)
+        color_hex = mix_color(n, d, s, as_hex=True)
+
+        for iso3 in region_to_iso[label]:
+            if iso3 == "ATA":
+                continue  # skip Antarctica
+            rows.append(
+                {
+                    "iso_a3": iso3,
+                    "macro_region": label,
+                    "Normative": n,
+                    "Deep": d,
+                    "Stochastic": s,
+                    "color_hex": color_hex,
+                }
+            )
+
+    if missing_regions:
+        print("[warn] Regions missing in JSON:", ", ".join(missing_regions))
+
+    map_df = pd.DataFrame(rows)
+    if map_df.empty:
+        raise ValueError("No regions found for this year that match the JSON mapping.")
+
+    unique_regions = map_df["macro_region"].unique()
+    region_to_idx = {reg: idx for idx, reg in enumerate(unique_regions)}
+    map_df["region_idx"] = map_df["macro_region"].map(region_to_idx)
+
+    n_regions = len(unique_regions)
+    colorscale = []
+    for region in unique_regions:
+        idx = region_to_idx[region]
+        start = idx / n_regions
+        end = (idx + 1) / n_regions
+        color_rgb = hex_to_rgb(
+            map_df.loc[map_df["macro_region"] == region, "color_hex"].iloc[0]
+        )
+        colorscale.append([start, color_rgb])
+        colorscale.append([end, color_rgb])
+
+    choropleth = go.Choropleth(
+        locations=map_df["iso_a3"],
+        z=map_df["region_idx"],
+        text=map_df["macro_region"],
+        hovertemplate="<b>%{text}</b><extra></extra>",
+        colorscale=colorscale,
+        showscale=False,
+        marker=dict(line=dict(color="rgba(255,255,255,0.7)", width=0.4)),
+    )
+
+    fig = go.Figure(data=choropleth)
+    fig.update_layout(
+        showlegend=False,
+        paper_bgcolor="#f8f8f8",
+        plot_bgcolor="#f8f8f8",
+        margin=dict(l=0, r=0, t=0, b=0),
+        geo=dict(
+            projection=dict(type=projection_type),
+            showframe=False,
+            showcoastlines=False,
+            bgcolor="#f8f8f8",
+            landcolor="#f8f8f8",
+        ),
+    )
+    return fig
+
+
+# =============================================================================
+# 4. Master routine
+# =============================================================================
+def generate_uncertainty_visualizations(
+    base_dir: str,
+    region_mapping_path: str,
+    stat: str = "raw",
+    model_type: str = "final",
+    years: Iterable[int] = (2030, 2050, 2070, 2100),
+    ternary_scale: int = 8,
+    quantize: bool = True,
+    annotate_points: bool = False,
+    marker_size: float = 18,
+    jitter_strength: float = 0.02,
+    random_state: int = 0,
+    output_dir: str = "fig_ternary_choropleth",
+):
+    shares_df = load_regional_uncertainty_shares(
+        base_dir=base_dir,
+        stat=stat,
+        model_type=model_type,
+        years=years,
+    )
+
+    output_base = Path(output_dir)
+    output_base.mkdir(parents=True, exist_ok=True)
+
+    sns.set_theme(style="white")
+
+    results = {}
+    last_map = None
+    for yr in years:
+        df_year = shares_df[shares_df["Year"] == yr]
+        if df_year.empty:
+            print(f"[warn] No data for year {yr}. Skipping.")
+            continue
+
+        fig_tern, ax_tern = draw_ternary_background(scale=ternary_scale)
+        add_regions_to_ternary(
+            ax_tern,
+            df_year,
+            scale=ternary_scale,
+            quantize=quantize,
+            annotate=annotate_points,
+            marker_size=marker_size,
+            jitter_strength=jitter_strength,
+            random_state=random_state,
+        )
+        ax_tern.set_title(f"Regional Uncertainty Composition — {yr}")
+        ternary_path = output_base / f"ternary_{yr}.svg"
+        fig_tern.savefig(ternary_path, dpi=300, bbox_inches="tight")
+        plt.close(fig_tern)
+
+        fig_map = build_choropleth(
+            df_year,
+            region_to_iso_path=region_mapping_path,
+            scale=ternary_scale,
+            quantize=quantize,
+        )
+        svg_map_path = output_base / f"choropleth_{yr}.svg"
+        fig_map.write_image(str(svg_map_path), format="svg", width=800, height=600)
+
+        results[yr] = {"ternary": ternary_path, "choropleth": svg_map_path}
+        last_map = fig_map
+
+    return last_map, results
+
+
+def plot_grouped_stacked_feature_importance_from_csvs(
+    base_dir,
+    scope="global",
+    stat="mean",
+    model_type="final",
+    years=(2030, 2050, 2070, 2100),
+    region=None,
+    output_file=None,
+    normalized=True,
+    figsize=(9, 4),
+    bar_width=1.0,
+    legend_fontsize=9,
+    feature_colors=None,
+    feature_order=None,
+    group_map: Dict[str, List[str]] = None,
+    group_colors: Dict[str, str] = None,
+    # Default feature order and colors if no grouping is provided
+    FEATURE_ORDER=["Scenario", "Regret", "Welfare", "Optimization", "Sample"],
+    FEATURE_COLORS={
+        "Scenario": "#8da0cb",
+        "Regret": "#b2e2e2",
+        "Welfare": "#66c2a4",
+        "Optimization": "#238b45",
+        "Sample": "#fc8d62",
+    },
+    # Default colors for grouped bars; feel free to tweak
+    GROUP_COLORS={
+        "Deep Uncertainty": "#8da0cb",
+        "Normative Uncertainty": "#238b45",
+        "Stochastic Uncertainty": "#fc8d62",
+    },
+):
+    """
+    Builds a stacked bar chart with one bar per year from saved SHAP CSVs.
+    If `group_map` is provided, features are aggregated by group and the legend
+    shows entries like "Deep Uncertainty (Scenario)".
+    """
+    # Ensure years are integers and not floats
+    years = [int(yr) for yr in years]
+
+    year_order = list(years)
+    feature_order = feature_order if feature_order is not None else FEATURE_ORDER
+    feature_colors = feature_colors if feature_colors is not None else FEATURE_COLORS
+
+    kind = "shap_full" if model_type == "final" else "shap_cv"
+    root = Path(base_dir) / scope.lower() / stat.lower()
+    if not root.exists():
+        raise FileNotFoundError(f"Directory not found: {root}")
+
+    def read_importance_csv(path: Path):
+        if not path.exists():
+            return None
+        df = pd.read_csv(path)
+        if not {"Feature", "Importance"}.issubset(df.columns):
+            raise ValueError(f"{path} must contain 'Feature' and 'Importance' columns")
+        s = df.set_index("Feature")["Importance"]
+        return s.reindex(feature_order, fill_value=0.0)
+
+    sns.set_theme(style="white")
+
+    if group_map:
+        group_order = list(group_map.keys())
+        label_map = {
+            group: f"{group} ({', '.join(group_map[group])})" for group in group_order
+        }
+        group_colors = group_colors if group_colors is not None else GROUP_COLORS
+
+        def aggregate_by_group(df_row):
+            row = {"Year": df_row["Year"]}
+            for group, feats in group_map.items():
+                missing = [f for f in feats if f not in df_row.index]
+                if missing:
+                    raise KeyError(
+                        f"Missing features {missing} required for group '{group}'"
+                    )
+                row[group] = df_row[list(feats)].sum()
+            return row
+
+        def transform_df(df_plot):
+            records = [aggregate_by_group(row) for _, row in df_plot.iterrows()]
+            return (
+                pd.DataFrame(records),
+                group_order,
+                group_colors,
+                label_map,
+                group_map,
+            )
+
+    else:
+
+        def transform_df(df_plot):
+            return df_plot, feature_order, feature_colors, None, None
+
+    def plot_df(raw_df, title=None, outfile=None):
+        df_plot = raw_df.copy()
+        df_plot["Year"] = pd.Categorical(
+            df_plot["Year"], categories=year_order, ordered=True
+        )
+        df_plot = df_plot.sort_values("Year")
+
+        df_stack, stack_order, colors_map, legend_labels, legend_features = (
+            transform_df(df_plot)
+        )
+
+        fig, ax = plt.subplots(figsize=figsize)
+        x_pos = np.arange(len(df_stack))
+        bottoms = np.zeros(len(df_stack))
+
+        for key in stack_order:
+            values = df_stack[key].to_numpy()
+            ax.bar(
+                x_pos,
+                values,
+                width=bar_width,
+                bottom=bottoms,
+                color=colors_map.get(key, "#999999"),
+                label=legend_labels[key] if legend_labels else key,
+                align="center",
+            )
+            bottoms += values
+
+        ax.set_xticks(x_pos)
+        ax.set_xticklabels([str(y) for y in df_stack["Year"]])
+        ax.set_xlim(-0.5, len(df_stack) - 0.5)
+        ax.margins(x=0)
+        sns.despine(ax=ax, top=True, right=True, left=False, bottom=False)
+        ax.set_xlabel("")
+        ax.set_ylabel("Importance" + (" (normalized)" if normalized else ""))
+        if title:
+            ax.set_title(title)
+
+        handles, labels = ax.get_legend_handles_labels()
+        unique = dict(zip(labels, handles))
+        ordered_handles = [unique[lbl] for lbl in labels if lbl in unique]
+        ax.legend(
+            ordered_handles,
+            [lbl for lbl in labels if lbl in unique],
+            frameon=False,
+            fontsize=legend_fontsize,
+            ncol=1,
+            loc="upper left",
+            bbox_to_anchor=(1.02, 1.0),
+            borderaxespad=0.0,
+        )
+
+        if outfile:
+            print(f"[info] Saving figure to {outfile}")
+            Path(outfile).parent.mkdir(parents=True, exist_ok=True)
+            fig.savefig(outfile, dpi=300, bbox_inches="tight")
+            plt.close(fig)
+        else:
+            plt.show()
+
+        return fig
+
+    if scope.lower() == "global":
+        rows = []
+        for yr in year_order:
+            fpath = root / f"global_{yr}_{kind}.csv"
+            s = read_importance_csv(fpath)
+            if s is None:
+                continue
+            rows.append(
+                {"Year": yr, **{feat: float(s[feat]) for feat in feature_order}}
+            )
+
+        if not rows:
+            raise FileNotFoundError(
+                f"No CSVs found for scope=global, stat={stat}, kind={kind} in {root}"
+            )
+
+        df_plot = pd.DataFrame(rows)
+        fig = plot_df(df_plot, outfile=output_file)
+        return {"data": df_plot, "figure": fig}
+
+    pattern = re.compile(rf"^(?P<region>.+)_(?P<year>\d{{4}})_{kind}\.csv$")
+    files = [p for p in root.glob("*.csv") if p.is_file()]
+    region_set = set()
+    for p in files:
+        m = pattern.match(p.name)
+        if not m:
+            continue
+        yy = int(m.group("year"))
+        if yy in years:
+            region_set.add(m.group("region"))
+
+    region_list = [region] if region else sorted(region_set)
+    if not region_list:
+        raise FileNotFoundError(
+            f"No regional CSVs found for stat={stat}, kind={kind} in {root}"
+        )
+
+    figs = {}
+    all_rows = []
+
+    for rgn in region_list:
+        rows = []
+        for yr in year_order:
+            fpath = root / f"{rgn}_{yr}_{kind}.csv"
+            s = read_importance_csv(fpath)
+            if s is None:
+                continue
+            rows.append(
+                {
+                    "Region": rgn,
+                    "Year": yr,
+                    **{feat: float(s[feat]) for feat in feature_order},
+                }
+            )
+        if not rows:
+            continue
+
+        df_plot = pd.DataFrame(rows)
+        title = rgn.replace("_", " ")
+        out = None
+        if output_file:
+            outpath = Path(output_file)
+            out = str(Path(outpath.parent) / f"{outpath.stem}_{rgn}{outpath.suffix}")
+
+        fig = plot_df(df_plot, title=title, outfile=out)
+        figs[rgn] = fig
+        all_rows.append(df_plot)
+
+    df_all = pd.concat(all_rows, ignore_index=True) if all_rows else pd.DataFrame()
+    return {"data": df_all, "figure": figs}
+
+
+def render_all_grouped_stacked_charts(
+    base_dir,
+    scope="global",
+    stat="mean",
+    model_type="final",
+    years=(2030, 2050, 2070, 2100),
+    output_dir=None,
+    normalized=True,
+    figsize=(9, 6),
+    legend_fontsize=9,
+    bar_width=1.0,
+    group_map: Dict[str, List[str]] = None,
+):
+    if output_dir is not None:
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    kwargs = dict(
+        base_dir=base_dir,
+        stat=stat,
+        model_type=model_type,
+        years=years,
+        output_file=None if output_dir is None else "",
+        normalized=normalized,
+        figsize=figsize,
+        legend_fontsize=legend_fontsize,
+        bar_width=bar_width,
+        group_map=group_map,
+    )
+    if scope.lower() == "global":
+        outfile = (
+            None
+            if output_dir is None
+            else str(Path(output_dir) / f"global_{stat}_{model_type}_stacked.svg")
+        )
+        kwargs["scope"] = "global"
+        kwargs["output_file"] = outfile
+        return plot_grouped_stacked_feature_importance_from_csvs(**kwargs)
+    else:
+        outfile = (
+            None
+            if output_dir is None
+            else str(Path(output_dir) / f"regional_{stat}_{model_type}_stacked.svg")
+        )
+        kwargs["scope"] = "regional"
+        kwargs["output_file"] = outfile
+        return plot_grouped_stacked_feature_importance_from_csvs(**kwargs)
 
 
 def plot_emission_control_rate(
@@ -794,6 +1517,9 @@ def add_traces(
 
     if saving:
         filename = data_paths[0].split("/")[-1].split(".")[0] + "_" + output_name_suffix
+        # Check if output path exists
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
         # Save the plot
         fig.write_image(f"{output_path}/{filename}.svg")
 
@@ -1616,24 +2342,6 @@ def visualize_tradeoffs(
             ].shape[0]
         print("Adjusted top indices:", top_indices)
 
-        # for file in input_data:
-        #     df_type = concatenated_df[concatenated_df["type"] == file]
-
-        #     top_indices[file] = (
-        #         df_type[objective_of_interest]
-        #         .nsmallest(int(df_type.shape[0] * top_percentage))
-        #         .index
-        #     )
-        # print(file, len(top_indices[file]))
-
-        # Now within the top_indices, find the index with lowest years_above_temperature_threshold
-        # if temperature_filter:
-        #     index = df_type.loc[top_indices[file]][
-        #         "years_above_temperature_threshold"
-        #     ].idxmin()
-        #     print(index)
-        #     top_indices[file] = [index]
-
     if scaling:
         # Printing min max values of the objectives
         print("Min and Max values of the objectives", list_of_objectives)
@@ -1669,11 +2377,11 @@ def visualize_tradeoffs(
                     # this is one of your highlighted solutions:
                     file_color = color_mapping[_type]
                     # make it thicker
-                    lw = linewidth * 3  # or whatever factor you like
+                    lw = linewidth * highlight_factor  # or whatever factor you like
                     break
 
             # gray lines at half‐opacity, best ones fully opaque
-            alpha_here = 0.2 if file_color == "gray" else 1.0
+            alpha_here = alpha if file_color == "gray" else 1.0
 
         else:
             file_color = color_mapping.get(row["type"], "green")
@@ -1722,7 +2430,6 @@ def visualize_tradeoffs(
             os.makedirs(path_to_output)
         # Save the plot as svg
         plt.savefig(path_to_output + "/" + output_file_name, dpi=300)
-        # plt.savefig(path_to_output + "/" + output_file_name, dpi=300)
 
     # Show the plot
     plt.show()
@@ -2354,6 +3061,8 @@ def plot_choropleth_2D_data(
     normalized_colorbar=False,
     tickvals=[0, 0.25, 0.5, 0.75, 1],
     ticktext=["0%", "25%", "50%", "75%", "100%"],
+    plot_saving_format="svg",
+    show_frame=False,
 ):
 
     # Assert if input_data list and output_titles list is None
@@ -2473,28 +3182,56 @@ def plot_choropleth_2D_data(
                     "x": 0.5,
                     "y": 0.95,
                 },
+                # Remove the frame around the map
+                geo=dict(showframe=False),
             )
+
+            if show_frame:
+                fig.update_layout(
+                    geo=dict(showframe=True, framecolor="black", framewidth=1)
+                )
         else:
-            fig.update_layout(title_text="")
+            fig.update_layout(
+                title_text="",
+                geo=dict(showframe=False),
+            )
+
+            if show_frame:
+                fig.update_layout(
+                    geo=dict(showframe=True, framecolor="black", framewidth=1)
+                )
 
         # Policy index number
         filename = file.split(".")[0]
-        # filename = (
-        #     filename.split("_")[0]
-        #     + filename.split("_")[1]
-        #     + "_"
-        #     + filename.split("_")[-1]
-        # )
+
+        # TODO: This is a hotfix - change this later
+        # filename = filename.split("_")
+        # # Keep index 0 to 5 and the last element and combine them to create the new filename string
+        # filename = "_".join(filename[0:6] + [filename[11]] + [filename[-1]])
+        # print("Saving file: ", filename)
 
         output_file_name = filename
         if saving:
-            fig.write_image(
-                path_to_output
-                + "/"
-                + output_file_name
-                + str(year_to_visualize)
-                + ".svg"
-            )
+            # Check if path to output exists
+            if not os.path.exists(path_to_output):
+                os.makedirs(path_to_output)
+
+            if plot_saving_format == "png":
+                fig.write_image(
+                    path_to_output
+                    + "/"
+                    + output_file_name
+                    + str(year_to_visualize)
+                    + ".png"
+                )
+            else:
+                fig.write_image(
+                    path_to_output
+                    + "/"
+                    + output_file_name
+                    + str(year_to_visualize)
+                    + ".svg"
+                )
 
     return fig, processed_data_dict
 
@@ -3446,9 +4183,6 @@ def plot_stacked_area_chart_with_baseline_emissions(
                 # Concatenate the dataframes abaated_emissions and data but keep the similar region names together
                 data = pd.concat([data, abated_emissions])
 
-                # # Use string similarity to sort the regions
-                # data = data.reindex(sorted(data.index, key=lambda x: x.split("_")[0]))
-
                 # Shape of the data
 
             print("Region list: ", region_list)
@@ -3469,10 +4203,6 @@ def plot_stacked_area_chart_with_baseline_emissions(
                 color_discrete_sequence=colour_palette,
                 groupnorm=groupnorm,
                 category_orders={"variable": region_list},
-                # pattern_shape=data.index,
-                # Pattern Shape sequence for only the abated emissions
-                # pattern_shape_sequence=["x", None, "x", None, "x", None, "x", None, "x", None, "x", None, "x", None, "x", None, "x", None],
-                # pattern_shape_sequence=["x"],
             )
             if groupnorm is None:
                 fig.update_layout(yaxis_range=[yaxis_lower_limit, yaxis_upper_limit])
@@ -4303,162 +5033,920 @@ def plot_regret_heatmap(
     return ax
 
 
-# def plot_hypervolume(
-#     path_to_data="data/convergence_metrics",
-#     path_to_output="./data/plots/convergence_plots",
-#     input_data=[],  # Provide the list of input data files with extension
-#     xaxis_title="Number of Function Evaluations",
-#     yaxis_title="Hypervolume",
-#     linewidth=3,
-#     colour_palette=px.colors.qualitative.Dark24,
-#     template="plotly_white",
-#     yaxis_upper_limit=0.7,
-#     title_x=0.5,
-#     width=1000,
-#     height=800,
-#     fontsize=15,
-#     saving=False,
-# ):
-#     # Assert if input_data list is empty
-#     assert input_data, "No input data provided for visualization."
-
-#     # Loop through the input data list and load the data
-#     for idx, file in enumerate(input_data):
-#         data = pd.read_csv(path_to_data + "/" + file)
-#         # Keep only nfe and hypervolume columns
-#         data = data[["nfe", "hypervolume"]]
-#         data = data.sort_values(by="nfe")
-
-#         # Find the max nfe value
-#         nfe_max = data["nfe"].max()
-#         # Get title text from filename
-#         titletext = file.split("_")[0]
-#         # Convert the titletext from all uppercase to title case
-#         titletext = titletext.title()
-
-#         fig = go.Figure(
-#             data=[
-#                 go.Scatter(
-#                     x=data["nfe"],
-#                     y=data["hypervolume"],
-#                     fill="none",
-#                     mode="lines",  #'none',
-#                     line=dict(color=colour_palette[idx], width=linewidth),
-#                     showlegend=False,
-#                 )
-#             ]
-#         )
-
-#         # Set the chart title and axis labels
-#         fig.update_layout(
-#             title=dict(text=titletext),
-#             xaxis_title=xaxis_title,
-#             yaxis_title=yaxis_title,
-#             width=width,
-#             height=height,
-#             template=template,
-#             yaxis_range=[0, yaxis_upper_limit],
-#             title_x=title_x,
-#             font=dict(size=fontsize),
-#         )
-
-#         # Avoid zero tick in the y-axis - minor cosmetic change
-#         fig.update_yaxes(tickvals=(np.arange(0, yaxis_upper_limit, 0.1))[1:])
-
-#         # Save the figure
-#         if not os.path.exists(path_to_output):
-#             os.makedirs(path_to_output)
-
-#         if saving:
-#             output_file_name = f"{titletext}_{nfe_max}_hypervolume_plot"
-#             fig.write_image(path_to_output + "/" + output_file_name + ".png")
-
-#     return fig
+def plot_regional_emissions_comparison_with_boxplots(
+    data_paths,  # List of paths for the data
+    start_year,
+    end_year,
+    data_timestep,
+    timestep,
+    visualization_start_year,
+    visualization_end_year,
+    yaxis_range,
+    opacity,
+    plot_title,
+    xaxis_title,
+    yaxis_title,
+    template,
+    width,
+    height,
+    baseline_path=None,
+    colors=[
+        "rgba(252, 141, 98, 0.8)",
+        "rgba(93, 105, 177, 0.8)",
+        "rgba(218, 165, 27, 0.8)",
+        "rgba(47, 138, 196, 0.8)",
+        "rgba(153, 201, 69, 0.8)",
+    ],
+    median_colors=[
+        "rgba(252, 141, 98, 1)",
+        "rgba(93, 105, 177, 1)",
+        "rgba(218, 165, 27, 1)",
+        "rgba(47, 138, 196, 1)",
+        "rgba(153, 201, 69, 1)",
+    ],
+    baseline_color="gray",
+    fontsize=18,
+    column_widths=[0.8, 0.2],
+    output_path=None,
+    saving=False,
+    show_min_max=True,
+    region_dict=None,  # NEW
+    region_name=None,  # NEW
+    region_aggregation=False,  # NEW
+    output_filename=None,
+):
+
+    # Set the time horizon
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    list_of_years = time_horizon.model_time_horizon
+
+    data_loader = DataLoader()
+
+    # ---------------------------
+    # Baseline (regional optional)
+    # ---------------------------
+    if baseline_path:
+        baseline = np.load(baseline_path)
 
+        if region_aggregation:
+            assert (
+                region_dict is not None
+            ), "region_dict required if region_aggregation=True"
+            region_list, baseline = justice_region_aggregator(
+                data_loader=data_loader, region_config=region_dict, data=baseline
+            )
+            # baseline shape: (regions, years, ensemble) or (regions, years)
+            if baseline.ndim == 3:
+                baseline = baseline.mean(axis=2)
+
+            # select one region
+            if region_name is not None:
+                r_idx = region_list.index(region_name)
+                baseline = baseline[r_idx]
+
+        # --- make sure baseline is 2D (years x ensemble) ---
+        baseline = np.asarray(baseline)
+        if baseline.ndim == 1:
+            baseline = baseline[:, None]  # (years, 1)
+        elif baseline.shape[0] != len(list_of_years) and baseline.shape[1] == len(
+            list_of_years
+        ):
+            baseline = baseline.T
+
+        # same as before
+        baseline = pd.DataFrame(baseline.T, columns=list_of_years)
+        baseline = baseline.loc[:, visualization_start_year:visualization_end_year]
+        baseline = baseline.T
+        baseline = baseline.mean(axis=1)
 
-if __name__ == "__main__":
+    # ---------------------------
+    # Load the data and create dataframes
+    # ---------------------------
+    data_frames = []
+    for path in data_paths:
+        filetype = os.path.splitext(path)[1]
+        if filetype == ".npy":
+            data = np.load(path)
+        elif filetype == ".pkl":
+            with open(path, "rb") as f:
+                data = pickle.load(f)
+        elif filetype == ".csv":
+            data = pd.read_csv(path)
 
-    fig, data = plot_choropleth(
-        variable_name="constrained_emission_control_rate",
-        path_to_data="data/reevaluation/only_welfare_temp/",  # "data/reevaluation/balanced/",  # "data/reevaluation",
-        path_to_output="./data/plots/regional/only_welfare_temp",
-        projection="natural earth",
-        # scope='usa',
-        year_to_visualize=2100,
-        input_data=[
-            "UTILITARIAN_reference_set_idx16.pkl",
-            "PRIORITARIAN_reference_set_idx196.pkl",
-            "SUFFICIENTARIAN_reference_set_idx57.pkl",
-            "EGALITARIAN_reference_set_idx404.pkl",
-        ],
-        output_titles=[
-            "Utilitarian",
-            "Prioritarian",
-            "Sufficientarian",
-            "Egalitarian",
-        ],
-        title="Mitigation Burden Distribution in ",
-        data_label="Emission Control Rate",
-        colourmap="OrRd",
-        legend_label="\n",
-        # scenario_list= ['SSP245'],
-        scenario_list=[
-            "SSP119",
-            "SSP126",
-            "SSP245",
-            "SSP370",
-            "SSP434",
-            "SSP460",
-            "SSP534",
-            "SSP585",
-        ],  # ['SSP119', 'SSP126', 'SSP245', 'SSP370', 'SSP434', 'SSP460', 'SSP534', 'SSP585'],
-        data_normalization=True,
-        saving=True,
-        show_colorbar=True,
-        show_title=False,
+        if region_aggregation:
+            assert (
+                region_dict is not None
+            ), "region_dict required if region_aggregation=True"
+            region_list, data = justice_region_aggregator(
+                data_loader=data_loader, region_config=region_dict, data=data
+            )
+
+            # data shape: (regions, years, ensemble)
+            if region_name is not None:
+                r_idx = region_list.index(region_name)
+                data = data[r_idx]
+
+        # original behaviour
+        if len(data.shape) == 3:
+            data = np.sum(data, axis=0)
+
+        data = data.T
+        df = pd.DataFrame(data, columns=list_of_years).loc[
+            :, visualization_start_year:visualization_end_year
+        ]
+        data_frames.append(df.T)
+
+    # ---- rest of your code unchanged ----
+    fig = make_subplots(
+        rows=1, cols=2, column_widths=column_widths, subplot_titles=[plot_title, " "]
     )
 
-    ############################################################################################################
-    # fig = plot_timeseries(
-    #     path_to_data="data/reevaluation/only_welfare_temp/",  # "data/reevaluation",  # /balanced,  # "data/reevaluation",
-    #     path_to_output="./data/plots/regional/only_welfare_temp",  # "./data/plots/regional",
-    #     x_label="Years",
-    #     y_label="Temperature Rise (°C)",
-    #     variable_name="global_temperature",
-    #     input_data=[
-    #         "UTILITARIAN_reference_set_idx16.pkl",
-    #         "PRIORITARIAN_reference_set_idx196.pkl",
-    #         # "UTILITARIAN_reference_set_idx51.pkl",
-    #         # "UTILITARIAN_reference_set_idx51_idx62.pkl",
-    #         # "PRIORITARIAN_reference_set_idx817.pkl",
-    #         # "PRIORITARIAN_reference_set_idx817_idx59.pkl",
-    #         # "UTILITARIAN_reference_set_idx88.pkl",
-    #         # "PRIORITARIAN_reference_set_idx748.pkl",
-    #         # "SUFFICIENTARIAN_reference_set_idx99.pkl",
-    #         # "EGALITARIAN_reference_set_idx147.pkl",
-    #     ],
-    #     output_titles=[
-    #         "Utilitarian",
-    #         "Prioritarian",
-    #         # "Sufficientarian",
-    #         # "Egalitarian",
-    #     ],
-    #     main_title="Global Temperature Rise - ",
-    #     show_title=False,
-    #     saving=True,
-    #     yaxis_lower_limit=0,
-    #     yaxis_upper_limit=6,
-    #     alpha=0.1,
-    #     linewidth=2.5,
-    #     start_year=2015,
-    #     end_year=2300,
-    #     visualization_start_year=2025,
-    #     visualization_end_year=2100,
-    #     scenario_list=[
-    #         "SSP119",
-    #         "SSP245",
-    #         "SSP370",
-    #         "SSP434",
-    #         "SSP585",
-    #     ],  # ['SSP119', 'SSP126', 'SSP245', 'SSP370', 'SSP434', 'SSP460', 'SSP534', 'SSP585'], # #
-    # )
+    for idx, emissions in enumerate(data_frames):
+        color = colors[idx]
+        median_color = median_colors[idx]
+
+        max_percentile = np.percentile(emissions, 100, axis=1)
+        min_percentile = np.percentile(emissions, 0, axis=1)
+        p75 = np.percentile(emissions, 75, axis=1)
+        p25 = np.percentile(emissions, 25, axis=1)
+
+        if show_min_max:
+            fig.add_trace(
+                go.Scatter(
+                    x=emissions.index,
+                    y=max_percentile,
+                    mode="lines",
+                    line=dict(color=color, width=0.5),
+                    fill=None,
+                    showlegend=False,
+                ),
+                row=1,
+                col=1,
+            )
+
+            fig.add_trace(
+                go.Scatter(
+                    x=emissions.index,
+                    y=min_percentile,
+                    mode="lines",
+                    line=dict(color=color, width=0.5),
+                    fill="tonexty",
+                    opacity=opacity * 0.01,
+                    showlegend=False,
+                ),
+                row=1,
+                col=1,
+            )
+
+        fig.add_trace(
+            go.Scatter(
+                x=emissions.index,
+                y=p75,
+                mode="lines",
+                line=dict(color=color, width=0.5),
+                fill=None,
+                showlegend=False,
+            ),
+            row=1,
+            col=1,
+        )
+
+        fig.add_trace(
+            go.Scatter(
+                x=emissions.index,
+                y=p25,
+                mode="lines",
+                line=dict(color=color, width=0.5),
+                fill="tonexty",
+                opacity=opacity,
+                showlegend=False,
+            ),
+            row=1,
+            col=1,
+        )
+
+        fig.add_trace(
+            go.Scatter(
+                x=emissions.index,
+                y=emissions.median(axis=1),
+                mode="lines",
+                line=dict(color=median_color, width=2),
+                name=f"Median {idx+1}",
+            ),
+            row=1,
+            col=1,
+        )
+
+        last_year_data = emissions.iloc[-1]
+        filename = data_paths[idx].split("/")[-1].split(".")[0]
+        filename = filename.split("_")[0]
+        fig.add_trace(
+            go.Box(
+                y=last_year_data,
+                name=filename,
+                marker=dict(color=median_color),
+                width=0.1,
+            ),
+            row=1,
+            col=2,
+        )
+
+    if baseline_path:
+        fig.add_trace(
+            go.Scatter(
+                x=emissions.index,
+                y=baseline,
+                mode="lines",
+                line=dict(color=baseline_color, width=2, dash="dash"),
+                name="Baseline",
+            ),
+            row=1,
+            col=1,
+        )
+
+    fig.update_traces(
+        marker=dict(line=dict(width=0.3, color=baseline_color)), row=1, col=2
+    )
+
+    fig.update_layout(
+        title=plot_title,
+        xaxis_title=xaxis_title,
+        yaxis_title=yaxis_title,
+        template=template,
+        height=height,
+        width=width,
+    )
+
+    fig.update_yaxes(
+        title_text=yaxis_title, range=yaxis_range, showgrid=False, row=1, col=1
+    )
+    fig.update_yaxes(
+        range=yaxis_range, showticklabels=False, showgrid=False, row=1, col=2
+    )
+    fig.update_xaxes(title_text=xaxis_title, showgrid=False, row=1, col=1)
+    fig.update_layout(font=dict(size=fontsize))
+    fig.update_layout(xaxis=dict(domain=[0, 0.8]), xaxis2=dict(domain=[0.95, 1]))
+
+    fig.update_xaxes(
+        showline=True, linewidth=1, linecolor="black", ticks="outside", row=1, col=1
+    )
+    fig.update_yaxes(
+        showline=True, linewidth=1, linecolor="black", ticks="outside", row=1, col=1
+    )
+
+    if saving:
+        if output_filename is None:
+            filename = "_".join(
+                [os.path.splitext(os.path.basename(path))[0] for path in data_paths]
+            )
+            fig.write_image(f"{output_path}/{filename}.svg")
+        else:
+            filename = "_".join(
+                [os.path.splitext(os.path.basename(path))[0] for path in data_paths]
+            )
+            fig.write_image(f"{output_path}/{filename}_{output_filename}.svg")
+
+    return fig
+
+
+if __name__ == "__main__":
+
+    fig, data = plot_choropleth(
+        variable_name="constrained_emission_control_rate",
+        path_to_data="data/reevaluation/only_welfare_temp/",  # "data/reevaluation/balanced/",  # "data/reevaluation",
+        path_to_output="./data/plots/regional/only_welfare_temp",
+        projection="natural earth",
+        # scope='usa',
+        year_to_visualize=2100,
+        input_data=[
+            "UTILITARIAN_reference_set_idx16.pkl",
+            "PRIORITARIAN_reference_set_idx196.pkl",
+            "SUFFICIENTARIAN_reference_set_idx57.pkl",
+            "EGALITARIAN_reference_set_idx404.pkl",
+        ],
+        output_titles=[
+            "Utilitarian",
+            "Prioritarian",
+            "Sufficientarian",
+            "Egalitarian",
+        ],
+        title="Mitigation Burden Distribution in ",
+        data_label="Emission Control Rate",
+        colourmap="OrRd",
+        legend_label="\n",
+        # scenario_list= ['SSP245'],
+        scenario_list=[
+            "SSP119",
+            "SSP126",
+            "SSP245",
+            "SSP370",
+            "SSP434",
+            "SSP460",
+            "SSP534",
+            "SSP585",
+        ],  # ['SSP119', 'SSP126', 'SSP245', 'SSP370', 'SSP434', 'SSP460', 'SSP534', 'SSP585'],
+        data_normalization=True,
+        saving=True,
+        show_colorbar=True,
+        show_title=False,
+    )
+
+
+def visualize_tradeoffs_colored(
+    input_data=[],
+    figsize=(15, 10),
+    set_style="whitegrid",
+    font_scale=1.8,
+    colourmap="bright",
+    linewidth=0.4,
+    alpha=0.1,
+    path_to_data="data/reevaluation/",
+    path_to_output="./data/plots/only_welfare_temp",
+    scaling=True,
+    feature_range=(0, 1),
+    column_labels=None,
+    legend_labels=None,
+    show_legend=False,
+    axis_rotation=30,
+    fontsize=12,
+    list_of_objectives=[
+        "welfare_utilitarian",
+        "years_above_temperature_threshold",
+        "damage_cost_per_capita_utilitarian",
+        "abatement_cost_per_capita_utilitarian",
+    ],
+    direction_of_optimization=[
+        "min",
+        "min",
+        "max",
+        "max",
+    ],
+    pretty_labels=[
+        "Welfare",
+        "Years Above Temp Threshold",
+        "Welfare Loss Damage",
+        "Welfare Loss Abatement",
+    ],
+    default_colors=["red", "blue"],
+    top_percentage=0.1,
+    objective_of_interest="fraction_above_threshold",
+    show_best_solutions=False,
+    highlight_indices=None,
+    highlight_factor=3,
+    saving=False,
+    custom_colors=["#d62728", "#ff7f0e", "#a8d2e8", "#05417d"],
+):
+
+    sns.set_theme(font_scale=font_scale)
+    sns.set_style(set_style)
+    sns.set_theme(rc={"figure.figsize": figsize})
+
+    # Assertions
+    assert input_data, "Input data not provided"
+    assert path_to_data, "Path to reference set is not provided"
+    assert len(list_of_objectives) == len(direction_of_optimization)
+
+    concatenated_df = pd.DataFrame()
+
+    for file in input_data:
+        data = pd.read_csv(path_to_data + "/" + file)
+        data = data[list_of_objectives]
+        data = np.abs(data)
+        data["type"] = file
+        concatenated_df = pd.concat([concatenated_df, data], axis=0)
+
+    concatenated_df.reset_index(drop=True, inplace=True)
+
+    if scaling:
+        scaler = MinMaxScaler(feature_range=feature_range)
+        concatenated_df[list_of_objectives] = scaler.fit_transform(
+            concatenated_df[list_of_objectives]
+        )
+
+        for i, direction in enumerate(direction_of_optimization):
+            if direction == "min":
+                concatenated_df[list_of_objectives[i]] = (
+                    1 - concatenated_df[list_of_objectives[i]]
+                )
+
+    # --- Custom colormap (red → orange → light blue → blue) ---
+    cmap = LinearSegmentedColormap.from_list("temp_scale", custom_colors)
+    norm = mcolors.Normalize(
+        vmin=concatenated_df[objective_of_interest].min(),
+        vmax=concatenated_df[objective_of_interest].max(),
+    )
+
+    limits = parcoords.get_limits(concatenated_df[list_of_objectives])
+    limits.columns = pretty_labels
+    axes = parcoords.ParallelAxes(limits, rot=axis_rotation, fontsize=fontsize)
+
+    # Adjust highlight indices if needed
+    top_indices = {}
+    if show_best_solutions and highlight_indices:
+        top_indices = highlight_indices.copy()
+        size_offset = 0
+        for file in input_data:
+            if file in top_indices:
+                top_indices[file] = [i + size_offset for i in top_indices[file]]
+            size_offset += concatenated_df[concatenated_df["type"] == file].shape[0]
+
+    for idx, row in concatenated_df.iterrows():
+        file_color = cmap(norm(row[objective_of_interest]))
+        lw = linewidth
+        alpha_here = alpha
+
+        if show_best_solutions and highlight_indices:
+            for _type, indices in top_indices.items():
+                if idx in indices:
+                    lw = linewidth * highlight_factor
+                    alpha_here = 1.0
+                    break
+
+        _sliced_data = pd.DataFrame(row[list_of_objectives].values).T
+        _sliced_data.columns = pretty_labels
+
+        axes.plot(
+            _sliced_data,
+            color=file_color,
+            linewidth=lw,
+            alpha=alpha_here,
+        )
+
+    if saving:
+        output_file_name = (
+            "tradeoffs_"
+            + "_".join([file.split("_")[0] for file in input_data])
+            + "_"
+            + ".svg"
+        )
+        if not os.path.exists(path_to_output):
+            os.makedirs(path_to_output)
+        plt.savefig(path_to_output + "/" + output_file_name, dpi=300)
+
+    plt.show()
+    return concatenated_df
+
+
+def _interp_hex_colors(colors, t):
+    """
+    Interpolate along a list of HEX colors at position t in [0, 1].
+    Returns a Plotly-compatible 'rgb(r,g,b)' string.
+    """
+    t = float(np.clip(t, 0.0, 1.0))
+    n = len(colors)
+    if n == 1:
+        r, g, b = pc.hex_to_rgb(colors[0])
+        return f"rgb({r},{g},{b})"
+
+    pos = t * (n - 1)
+    i = int(np.floor(pos))
+    j = min(i + 1, n - 1)
+    w = pos - i
+
+    c0 = np.array(pc.hex_to_rgb(colors[i]), dtype=float)
+    c1 = np.array(pc.hex_to_rgb(colors[j]), dtype=float)
+    c = (1.0 - w) * c0 + w * c1
+    r, g, b = np.round(c).astype(int)
+    return f"rgb({r},{g},{b})"
+
+
+
+
+
+def plot_alluvial_plotly(
+    df,
+    objectives,
+    direction_of_optimization,
+    temperature_col="fraction_above_threshold",
+    bins=5,  # int or dict like {"welfare_3": 3, "welfare_4": 3}
+    custom_colors=("#d62728", "#ff7f0e", "#9ecae1", "#08519c"),
+    title="Alluvial Trade‑offs (binned)",
+    bin_order="desc",  # "desc": 0.8-1.0 at top (encouraged), "asc": 0.0-0.2 at top
+    drop_unused_bins=True,  # removes empty middle bins per objective (recommended)
+):
+    """
+    Plot an alluvial/Sankey of binned objectives (Plotly).
+
+    - Scales objectives to [0,1] and inverts those with direction "min" so 1=best.
+    - Bins each objective into uniform bins. `bins` can be:
+        * int: same number of bins for all objectives
+        * dict: per-objective bins, e.g. {"welfare_3": 3, "welfare_4": 3}
+    - Colors links by mean scaled temp score of policies in that flow.
+    """
+
+    df = df.copy()
+
+    # Ensure objectives and temperature are numeric BEFORE scaling
+    cols_to_numeric = list(dict.fromkeys(objectives + [temperature_col]))
+    for c in cols_to_numeric:
+        df[c] = pd.to_numeric(df[c], errors="coerce")
+
+    df = df.dropna(subset=cols_to_numeric)
+    if df.empty:
+        raise ValueError(
+            "After coercing to numeric and dropping NaNs, the dataframe is empty."
+        )
+
+    # ---- scale objectives to 0..1 ----
+    scaler = MinMaxScaler()
+    df_scaled = df.copy()
+    df_scaled[objectives] = scaler.fit_transform(df_scaled[objectives])
+
+    # invert "min" objectives so that 1=best, 0=worst
+    for obj, direction in zip(objectives, direction_of_optimization):
+        if direction == "min":
+            df_scaled[obj] = 1.0 - df_scaled[obj]
+
+    # numeric temperature score for coloring
+    if temperature_col in objectives:
+        temp_score = df_scaled[temperature_col].astype(float).values
+    else:
+        tmp = df_scaled[[temperature_col]].copy()
+        tmp[temperature_col] = MinMaxScaler().fit_transform(tmp[[temperature_col]])
+        temp_score = tmp[temperature_col].astype(float).values
+    df_scaled["_temp_score"] = temp_score
+
+    if bin_order not in ("asc", "desc"):
+        raise ValueError("bin_order must be 'asc' or 'desc'")
+
+    # Helper to get bin count per objective
+    def _bins_for(obj):
+        if isinstance(bins, dict):
+            return int(bins.get(obj, 5))
+        return int(bins)
+
+    # ---- bin each objective into categories (possibly with different bin counts) ----
+    for obj in objectives:
+        k = _bins_for(obj)
+        edges = np.linspace(0.0, 1.0, k + 1)
+        labels = [f"{edges[i]:.1f}-{edges[i+1]:.1f}" for i in range(k)]
+
+        df_scaled[obj] = pd.cut(
+            df_scaled[obj],
+            bins=edges,
+            labels=labels,
+            include_lowest=True,
+        )
+
+        ordered_labels = labels if bin_order == "asc" else list(reversed(labels))
+        df_scaled[obj] = df_scaled[obj].cat.reorder_categories(
+            ordered_labels, ordered=True
+        )
+
+        if drop_unused_bins:
+            df_scaled[obj] = df_scaled[obj].cat.remove_unused_categories()
+
+    df_scaled = df_scaled.dropna(subset=objectives + ["_temp_score"])
+    if df_scaled.empty:
+        raise ValueError("After binning, the dataframe is empty (check scaling/bins).")
+
+    # ---- build nodes (use categories ACTUALLY PRESENT, in their ordered order) ----
+    node_labels = []
+    node_index = {}
+    for obj in objectives:
+        for lab in list(df_scaled[obj].cat.categories):
+            node_index[(obj, lab)] = len(node_labels)
+            node_labels.append(f"{obj}\n{lab}")
+
+    # ---- build links (aggregate counts + mean temp_score) ----
+    source, target, value, link_colors = [], [], [], []
+
+    for i in range(len(objectives) - 1):
+        left = objectives[i]
+        right = objectives[i + 1]
+
+        grouped = (
+            df_scaled.groupby([left, right], observed=True)["_temp_score"]
+            .agg(count="size", mean="mean")
+            .reset_index()
+        )
+
+        for _, row in grouped.iterrows():
+            s = node_index[(left, row[left])]
+            t = node_index[(right, row[right])]
+            source.append(s)
+            target.append(t)
+            value.append(int(row["count"]))
+
+            mean_val = float(np.clip(row["mean"], 0.0, 1.0))
+            link_colors.append(_interp_hex_colors(list(custom_colors), mean_val))
+
+    fig = go.Figure(
+        data=[
+            go.Sankey(
+                arrangement="perpendicular",  # keep auto layout (less jumbled) #snap
+                node=dict(
+                    pad=15,
+                    thickness=15,
+                    label=None,  # node_labels,
+                    color="lightgray",
+                ),
+                link=dict(
+                    source=source,
+                    target=target,
+                    value=value,
+                    color=link_colors,
+                ),
+            )
+        ]
+    )
+
+    fig.update_layout(title=title, font_size=10)
+    return fig
+
+
+def scale_and_orient_objectives(
+    df: pd.DataFrame,
+    objectives,
+    direction_of_optimization,
+    feature_range=(0, 1),
+    take_abs=True,
+):
+    assert len(objectives) == len(direction_of_optimization)
+
+    out = df.copy()
+
+    missing = [c for c in objectives if c not in out.columns]
+    if missing:
+        raise KeyError(f"Missing objective columns in df: {missing}")
+
+    for c in objectives:
+        out[c] = pd.to_numeric(out[c], errors="coerce")
+
+    out = out.dropna(subset=objectives)
+    if out.empty:
+        raise ValueError(
+            "No rows left after converting objectives to numeric and dropping NaNs."
+        )
+
+    if take_abs:
+        out[objectives] = out[objectives].abs()
+
+    scaler = MinMaxScaler(feature_range=feature_range)
+    out[objectives] = scaler.fit_transform(out[objectives])
+
+    for obj, direction in zip(objectives, direction_of_optimization):
+        if direction == "min":
+            out[obj] = 1.0 - out[obj]
+
+    return out
+
+
+
+
+def curved_parallel_coordinates_clustered(
+    df: pd.DataFrame,
+    objectives,
+    direction_of_optimization,
+    color_by,
+    columns_to_plot=None,  # if None -> plot objectives; you can hide temperature axis here
+    labels=None,
+    bins=3,  # int OR dict, e.g. {"welfare_3": 2, "welfare_4": 2, "default": 3}
+    jitter=0.0,  # small jitter around bin centers (e.g. 0.01–0.03) or 0.0
+    feature_range=(0, 1),
+    take_abs=False,
+    figsize=(12, 8),
+    alpha=0.25,
+    linewidth=1.0,
+    curvature=0.35,
+    highlight_indices=None,  # ORIGINAL row indices from your CSV (e.g. [9,52,86])
+    highlight_factor=3.5,
+    highlight_alpha=1.0,
+    custom_colors=("#d62728", "#ff7f0e", "#9ecae1", "#08519c"),
+    x_rotation=25,
+    title=None,
+    random_state=0,
+):
+    """
+    Same as before, but `bins` can now be:
+      - int: same bins for all plotted axes
+      - dict: per-axis bins with optional default, e.g.
+          bins={"welfare_0": 4, "welfare_3": 2, "welfare_4": 2, "default": 3}
+
+    Each axis is binned on [0,1] into uniform-width bins, values snapped to bin centers.
+    """
+
+    if columns_to_plot is None:
+        columns_to_plot = list(objectives)
+
+    if labels is None:
+        labels = list(columns_to_plot)
+
+    if len(labels) != len(columns_to_plot):
+        raise ValueError("labels must have the same length as columns_to_plot")
+
+    if len(objectives) != len(direction_of_optimization):
+        raise ValueError(
+            "objectives and direction_of_optimization must have the same length"
+        )
+
+    def _bins_for(colname: str) -> int:
+        if isinstance(bins, dict):
+            b = bins.get(colname, bins.get("default", 3))
+            return int(b)
+        return int(bins)
+
+    df0 = df.copy()
+    df0["_orig_index"] = df0.index  # preserve original row index for highlighting
+
+    # Ensure numeric for required cols
+    required_cols = list(dict.fromkeys(list(objectives) + [color_by]))
+    for c in required_cols:
+        if c not in df0.columns:
+            raise KeyError(f"Missing column in df: {c}")
+        df0[c] = pd.to_numeric(df0[c], errors="coerce")
+
+    # Drop rows with NaNs in required cols
+    df0 = df0.dropna(subset=required_cols)
+    if df0.empty:
+        raise ValueError("No rows left after numeric coercion and dropping NaNs.")
+
+    # Optional abs
+    if take_abs:
+        df0[objectives] = df0[objectives].abs()
+
+    # Scale objectives to feature_range
+    scaler = MinMaxScaler(feature_range=feature_range)
+    df_scaled = df0.copy()
+    df_scaled[objectives] = scaler.fit_transform(df_scaled[objectives])
+
+    # Invert "min" objectives so 1=best
+    for obj, direction in zip(objectives, direction_of_optimization):
+        if direction == "min":
+            df_scaled[obj] = 1.0 - df_scaled[obj]
+
+    # Ensure color_by is in [0,1] if it's not among objectives
+    if color_by not in objectives:
+        tmp_scaler = MinMaxScaler(feature_range=feature_range)
+        df_scaled[color_by] = tmp_scaler.fit_transform(df_scaled[[color_by]])
+
+    # -----------------------
+    # Cluster/bin axes to plot (PER AXIS)
+    # -----------------------
+    rng = np.random.default_rng(random_state)
+    df_clustered = df_scaled.copy()
+    clustered_cols = []
+
+    for col in columns_to_plot:
+        if col not in df_clustered.columns:
+            raise KeyError(f"columns_to_plot contains '{col}' which is not in df")
+
+        b = _bins_for(col)
+        if b < 2:
+            raise ValueError(f"bins for '{col}' must be >= 2 (got {b}).")
+
+        edges = np.linspace(0.0, 1.0, b + 1)
+        centers = (edges[:-1] + edges[1:]) / 2.0
+
+        vals = df_clustered[col].to_numpy(dtype=float)
+        vals = np.clip(vals, 0.0, 1.0)
+
+        idx = np.digitize(vals, edges, right=False) - 1
+        idx = np.clip(idx, 0, len(centers) - 1)
+        snapped = centers[idx]
+
+        if jitter and jitter > 0:
+            snapped = snapped + rng.uniform(-jitter, jitter, size=snapped.shape)
+            snapped = np.clip(snapped, 0.0, 1.0)
+
+        new_col = f"{col}__clustered_{b}bins"
+        df_clustered[new_col] = snapped
+        clustered_cols.append(new_col)
+
+    # -----------------------
+    # Plot curved coordinates
+    # -----------------------
+    data = df_clustered[clustered_cols].to_numpy(dtype=float)
+    x = np.arange(len(clustered_cols), dtype=float)
+
+    cmap = LinearSegmentedColormap.from_list("temp_scale", list(custom_colors))
+    norm = mcolors.Normalize(
+        vmin=float(df_clustered[color_by].min()),
+        vmax=float(df_clustered[color_by].max()),
+    )
+
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # vertical axes
+    for xi in x:
+        ax.vlines(xi, 0, 1, color="black", linewidth=0.8, alpha=0.35)
+
+    highlight_set = set(highlight_indices or [])
+
+    for row_i in range(len(df_clustered)):
+        y = data[row_i, :]
+
+        verts = [(x[0], y[0])]
+        codes = [mpath.Path.MOVETO]
+
+        for k in range(len(clustered_cols) - 1):
+            x0, y0 = x[k], y[k]
+            x1, y1 = x[k + 1], y[k + 1]
+            dx = x1 - x0
+
+            c1 = (x0 + curvature * dx, y0)
+            c2 = (x1 - curvature * dx, y1)
+
+            verts.extend([c1, c2, (x1, y1)])
+            codes.extend([mpath.Path.CURVE4, mpath.Path.CURVE4, mpath.Path.CURVE4])
+
+        path = mpath.Path(verts, codes)
+
+        base_color = cmap(norm(float(df_clustered.iloc[row_i][color_by])))
+
+        orig_idx = int(df_clustered.iloc[row_i]["_orig_index"])
+        is_hi = orig_idx in highlight_set
+
+        lw = linewidth * (highlight_factor if is_hi else 1.0)
+        a = highlight_alpha if is_hi else alpha
+
+        patch = PathPatch(
+            path,
+            facecolor="none",
+            edgecolor=base_color,
+            lw=lw,
+            alpha=a,
+            capstyle="round",
+            joinstyle="round",
+        )
+        ax.add_patch(patch)
+
+    ax.set_xlim(x[0] - 0.3, x[-1] + 0.3)
+    ax.set_ylim(0, 1)
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, rotation=x_rotation, ha="right")
+    ax.set_ylabel("Clustered (binned) value in [0,1]  (after inversion for 'min')")
+
+    if title:
+        ax.set_title(title)
+
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    plt.tight_layout()
+    plt.show()
+
+    return df_scaled, df_clustered, fig, ax
+
+
+
+
+
+def plot_colorbar_standalone(
+    df: pd.DataFrame,
+    color_by: str,
+    custom_colors=("#d62728", "#ff7f0e", "#9ecae1", "#08519c"),
+    figsize=(1.5, 5),
+    label=None,
+    orientation="vertical",
+    title=None,
+):
+    """
+    Plots a standalone colorbar matching the one used in
+    curved_parallel_coordinates_clustered().
+
+    Parameters
+    ----------
+    df        : The SAME df you pass to curved_parallel_coordinates_clustered
+                (before any internal scaling — i.e. your original df).
+    color_by  : Same value as used in the main plot.
+    custom_colors : Same tuple as used in the main plot.
+    figsize   : Figure size for the colorbar figure.
+    label     : Colorbar label. Defaults to the column name.
+    orientation : "vertical" or "horizontal".
+    title     : Optional figure title.
+    """
+    # --- Replicate exactly what the main function does ---
+    series = pd.to_numeric(df[color_by], errors="coerce").dropna()
+
+    cmap = LinearSegmentedColormap.from_list("temp_scale", list(custom_colors))
+    norm = mcolors.Normalize(vmin=float(series.min()), vmax=float(series.max()))
+
+    # --- Standalone figure with a single colorbar ---
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.set_visible(False)                         # hide the axes entirely
+
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])                              # required by matplotlib
+
+    cbar = fig.colorbar(
+        sm,
+        ax=ax,
+        orientation=orientation,
+        fraction=1.0,           # fill the whole figure
+        pad=0.0,
+    )
+    cbar.set_label(label if label is not None else color_by, fontsize=12)
+
+    if title:
+        fig.suptitle(title, fontsize=13)
+
+    plt.tight_layout()
+    plt.show()
+
+    return fig, cbar
diff --git a/justice/welfare/social_welfare_function.py b/justice/welfare/social_welfare_function.py
index 76a87fb..6c47d25 100644
--- a/justice/welfare/social_welfare_function.py
+++ b/justice/welfare/social_welfare_function.py
@@ -188,6 +188,48 @@ def calculate_state_disaggregated_welfare(self, consumption_per_capita, **kwargs
             welfare,
         )
 
+    def calculate_spatially_disaggregated_welfare(
+        self, consumption_per_capita, **kwargs
+    ):
+        """
+        This method calculates the welfare.
+        """
+        # Use np.maximum instead of np.where to be more efficient
+        consumption_per_capita = np.maximum(consumption_per_capita, SMALL_NUMBER)
+
+        # Aggregate the states dimension
+        states_aggregated_consumption_per_capita = self.states_aggregator(
+            consumption_per_capita,
+            self.climate_ensembles,
+            self.risk_aversion,
+        )
+
+        # Get the discount rate array
+        discount_rate = self.calculate_discount_rate(
+            pure_rate_of_social_time_preference=self.pure_rate_of_social_time_preference,
+            model_time_horizon=self.model_time_horizon,
+            timestep=self.timestep,
+        )
+
+        # TODO: Change that -1 later
+        # Calculate the welfare disaggregated temporally
+        spatially_disaggregated_welfare = np.zeros_like(
+            states_aggregated_consumption_per_capita
+        )
+
+        # If data is 3D, we need to apply the discount rate across the first axis (regions)
+        spatially_disaggregated_welfare = (
+            states_aggregated_consumption_per_capita - 1
+        ) * discount_rate[None, :]
+
+        # Calculate the welfare by summing across the temporal axis
+        spatially_disaggregated_welfare = np.sum(
+            spatially_disaggregated_welfare,
+            axis=1,
+        )
+
+        return spatially_disaggregated_welfare
+
     def calculate_stepwise_welfare(self, consumption_per_capita, timestep):
         """
         This method calculates the welfare.
diff --git a/poetry.lock b/poetry.lock
index 0d39170..448ae60 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -7,6 +7,7 @@ description = "Easily measure timing and throughput of code blocks, with beautif
 optional = false
 python-versions = ">=3.7, <4"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "about-time-4.2.1.tar.gz", hash = "sha256:6a538862d33ce67d997429d14998310e1dbfda6cb7d9bbfbf799c4709847fece"},
     {file = "about_time-4.2.1-py3-none-any.whl", hash = "sha256:8bbf4c75fe13cbd3d72f49a03b02c5c7dca32169b6d49117c257e7eb3eaee341"},
@@ -14,31 +15,33 @@ files = [
 
 [[package]]
 name = "absl-py"
-version = "2.3.0"
+version = "2.4.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "absl_py-2.3.0-py3-none-any.whl", hash = "sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3"},
-    {file = "absl_py-2.3.0.tar.gz", hash = "sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f"},
+    {file = "absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d"},
+    {file = "absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4"},
 ]
 
 [[package]]
 name = "alive-progress"
-version = "3.2.0"
+version = "3.3.0"
 description = "A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!"
 optional = false
 python-versions = "<4,>=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "alive-progress-3.2.0.tar.gz", hash = "sha256:ede29d046ff454fe56b941f686f89dd9389430c4a5b7658e445cb0b80e0e4deb"},
-    {file = "alive_progress-3.2.0-py3-none-any.whl", hash = "sha256:0677929f8d3202572e9d142f08170b34dbbe256cc6d2afbf75ef187c7da964a8"},
+    {file = "alive-progress-3.3.0.tar.gz", hash = "sha256:457dd2428b48dacd49854022a46448d236a48f1b7277874071c39395307e830c"},
+    {file = "alive_progress-3.3.0-py3-none-any.whl", hash = "sha256:63dd33bb94cde15ad9e5b666dbba8fedf71b72a4935d6fb9a92931e69402c9ff"},
 ]
 
 [package.dependencies]
 about-time = "4.2.1"
-grapheme = "0.6.0"
+graphemeu = "0.7.2"
 
 [[package]]
 name = "annotated-types"
@@ -47,6 +50,7 @@ description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -59,7 +63,7 @@ description = "Disable App Nap on macOS >= 10.9"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
-markers = "platform_system == \"Darwin\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_system == \"Darwin\""
 files = [
     {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
@@ -67,19 +71,20 @@ files = [
 
 [[package]]
 name = "asttokens"
-version = "3.0.0"
+version = "3.0.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
-    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
+    {file = "asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a"},
+    {file = "asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7"},
 ]
 
 [package.extras]
-astroid = ["astroid (>=2,<4)"]
-test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
+astroid = ["astroid (>=2,<5)"]
+test = ["astroid (>=2,<5)", "pytest (<9.0)", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "autograd"
@@ -88,6 +93,7 @@ description = "Efficiently computes derivatives of NumPy code."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "autograd-1.8.0-py3-none-any.whl", hash = "sha256:4ab9084294f814cf56c280adbe19612546a35574d67c574b04933c7d2ecb7d78"},
     {file = "autograd-1.8.0.tar.gz", hash = "sha256:107374ded5b09fc8643ac925348c0369e7b0e73bbed9565ffd61b8fd04425683"},
@@ -107,6 +113,7 @@ description = "Python Box2D"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "box2d-py-2.3.5.tar.gz", hash = "sha256:b37dc38844bcd7def48a97111d2b082e4f81cca3cece7460feb3eacda0da2207"},
     {file = "box2d_py-2.3.5-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:287aa54005c0644b47bf7ad72966e4068d66e56bcf8458f5b4a653ffe42a2618"},
@@ -117,196 +124,253 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2025.1.31"
+version = "2026.2.25"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
-    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
+    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
+    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
 ]
 
 [[package]]
 name = "cffi"
-version = "1.17.1"
+version = "2.0.0"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
-files = [
-    {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
-    {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
-    {file = "cffi-1.17.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382"},
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-    {file = "cffi-1.17.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3"},
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-    {file = "cffi-1.17.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e"},
-    {file = "cffi-1.17.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be"},
-    {file = "cffi-1.17.1-cp310-cp310-win32.whl", hash = "sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c"},
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+    {file = "charset_normalizer-3.4.6.tar.gz", hash = "sha256:1ae6b62897110aa7c79ea2f5dd38d1abca6db663687c0b1ad9aed6f6bae3d9d6"},
 ]
 
 [[package]]
@@ -316,6 +380,7 @@ description = "Clarabel Conic Interior Point Solver for Rust / Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "clarabel-0.11.1-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:c39160e4222040f051f2a0598691c4f9126b4d17f5b9e7678f76c71d611e12d8"},
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@@ -332,31 +397,15 @@ scipy = "*"
 
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 name = "click"
-version = "8.1.8"
-description = "Composable command line interface toolkit"
-optional = false
-python-versions = ">=3.7"
-groups = ["main"]
-markers = "python_version < \"3.11\""
-files = [
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-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
-[[package]]
-name = "click"
-version = "8.2.1"
+version = "8.3.1"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.dependencies]
@@ -364,26 +413,28 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cloudpickle"
-version = "3.1.1"
+version = "3.1.2"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414"},
 ]
 
 [[package]]
 name = "cma"
-version = "4.2.0"
+version = "4.4.4"
 description = "CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "cma-4.4.4.tar.gz", hash = "sha256:632bd654b5dce04c0eaa3166679d3e4773ce7a79eab7934e7f363c341b9a8170"},
 ]
 
 [package.dependencies]
@@ -392,6 +443,7 @@ numpy = "*"
 [package.extras]
 constrained-solution-tracking = ["moarchiving"]
 plotting = ["matplotlib"]
+statistical-tests = ["scipy"]
 
 [[package]]
 name = "colorama"
@@ -400,7 +452,7 @@ description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 groups = ["main"]
-markers = "platform_system == \"Windows\" or sys_platform == \"win32\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and (platform_system == \"Windows\" or sys_platform == \"win32\")"
 files = [
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@@ -408,171 +460,86 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.2.2"
+version = "0.2.3"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
-[package.dependencies]
-traitlets = ">=4"
-
 [package.extras]
 test = ["pytest"]
 
 [[package]]
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-version = "1.3.0"
-description = "Python library for calculating contours of 2D quadrilateral grids"
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-python-versions = ">=3.9"
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 [package.extras]
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 proxqp = ["proxsuite"]
 qoco = ["qoco"]
 scip = ["PySCIPOpt"]
 scipy = ["scipy"]
 testing = ["hypothesis", "pytest"]
-xpress = ["xpress"]
+xpress = ["xpress (>=9.5)"]
 
 [[package]]
 name = "cycler"
@@ -658,6 +627,7 @@ description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -674,6 +644,7 @@ description = "Distributed Evolutionary Algorithms in Python"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -703,80 +674,88 @@ numpy = "*"
 
 [[package]]
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+version = "1.8.20"
 description = "An implementation of the Debug Adapter Protocol for Python"
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "decorator"
-version = "5.2.1"
+version = "4.4.2"
 description = "Decorators for Humans"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [[package]]
 name = "deprecated"
-version = "1.2.18"
+version = "1.3.1"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.dependencies]
-wrapt = ">=1.10,<2"
+wrapt = ">=1.10,<3"
 
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools ; python_version >= \"3.12\"", "tox"]
 
 [[package]]
 name = "dill"
-version = "0.3.9"
+version = "0.4.1"
 description = "serialize all of Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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-    {file = "dill-0.3.9.tar.gz", hash = "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"},
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 ]
 
 [package.extras]
@@ -785,26 +764,33 @@ profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "docstring-parser"
-version = "0.16"
+version = "0.17.0"
 description = "Parse Python docstrings in reST, Google and Numpydoc format"
 optional = false
-python-versions = ">=3.6,<4.0"
+python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
+[package.extras]
+dev = ["pre-commit (>=2.16.0) ; python_version >= \"3.9\"", "pydoctor (>=25.4.0)", "pytest"]
+docs = ["pydoctor (>=25.4.0)"]
+test = ["pytest"]
+
 [[package]]
 name = "ema-workbench"
-version = "2.5.0"
+version = "2.5.3"
 description = "Exploratory modelling in Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "ema_workbench-2.5.3.tar.gz", hash = "sha256:e5ce847c3631a421cd1d3052bf6862e0ca3a0d58d2745de1ec4fb7c995abff80"},
 ]
 
 [package.dependencies]
@@ -819,60 +805,48 @@ statsmodels = "*"
 tqdm = "*"
 
 [package.extras]
-all = ["ema-workbench[cov,dev,docs,graph,jupyter,parallel]"]
+all = ["ema_workbench[cov,dev,docs,graph,jupyter,parallel]"]
 cov = ["coverage", "coveralls", "pytest-cov"]
-dev = ["ipyparallel", "jupyter-client", "pytest", "pytest-mock"]
+dev = ["ipyparallel", "jupyter_client", "pytest", "pytest-mock"]
 docs = ["myst", "myst-parser", "nbsphinx", "pyscaffold", "readthedocs-sphinx-search", "sphinx", "sphinx-rtd-theme"]
 graph = ["altair", "graphviz", "pydot"]
-jupyter = ["ipykernel", "ipython", "jupyter", "jupyter-client"]
+jupyter = ["ipykernel", "ipython", "jupyter", "jupyter_client"]
 netlogo = ["jpype-1", "pynetlogo"]
 parallel = ["ipyparallel", "traitlets"]
 pysd = ["pysd"]
-recommended = ["ema-workbench[dev,graph,jupyter]"]
+recommended = ["ema_workbench[dev,graph,jupyter]"]
 simio = ["pythonnet"]
 
-[[package]]
-name = "eval-type-backport"
-version = "0.2.2"
-description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
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+
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 test = ["pytest (>=6)"]
 
 [[package]]
 name = "executing"
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+version = "2.2.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
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@@ -885,6 +859,7 @@ description = "Finite-amplitude Impulse Response (FaIR) simple climate model"
 optional = false
 python-versions = ">=3.8, <4"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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@@ -913,6 +888,7 @@ description = "Notifications for all Farama Foundation maintained libraries."
 optional = false
 python-versions = "*"
 groups = ["main"]
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@@ -920,119 +896,121 @@ files = [
 
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 [[package]]
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 [package.extras]
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+all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.45.0)", "unicodedata2 (>=17.0.0) ; python_version <= \"3.14\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
 interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""]
 lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
-repacker = ["uharfbuzz (>=0.23.0)"]
+repacker = ["uharfbuzz (>=0.45.0)"]
 symfont = ["sympy"]
 type1 = ["xattr ; sys_platform == \"darwin\""]
-ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""]
+unicode = ["unicodedata2 (>=17.0.0) ; python_version <= \"3.14\""]
 woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "fsspec"
-version = "2025.5.1"
+version = "2026.2.0"
 description = "File-system specification"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.extras]
@@ -1040,12 +1018,12 @@ abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-dev = ["pre-commit", "ruff"]
+dev = ["pre-commit", "ruff (>=0.5)"]
 doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs (>2024.2.0)", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs (>2024.2.0)", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
-gcs = ["gcsfs"]
+gcs = ["gcsfs (>2024.2.0)"]
 git = ["pygit2"]
 github = ["requests"]
 gs = ["gcsfs"]
@@ -1054,13 +1032,13 @@ hdfs = ["pyarrow (>=1)"]
 http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
-s3 = ["s3fs"]
+s3 = ["s3fs (>2024.2.0)"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
 test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
 test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
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+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "backports-zstd ; python_version < \"3.14\"", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas (<3.0.0)", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -1070,6 +1048,7 @@ description = "Git Object Database"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -1080,14 +1059,15 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.44"
+version = "3.1.46"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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-    {file = "gitpython-3.1.44.tar.gz", hash = "sha256:c87e30b26253bf5418b01b0660f818967f3c503193838337fe5e573331249269"},
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 ]
 
 [package.dependencies]
@@ -1095,85 +1075,102 @@ gitdb = ">=4.0.1,<5"
 
 [package.extras]
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+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock ; python_version < \"3.8\"", "mypy (==1.18.2) ; python_version >= \"3.9\"", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions ; python_version < \"3.11\""]
 
 [[package]]
-name = "grapheme"
-version = "0.6.0"
+name = "graphemeu"
+version = "0.7.2"
 description = "Unicode grapheme helpers"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.extras]
-test = ["pytest", "sphinx", "sphinx-autobuild", "twine", "wheel"]
+dev = ["pytest"]
+docs = ["sphinx", "sphinx-autobuild"]
 
 [[package]]
 name = "grpcio"
-version = "1.73.0"
+version = "1.78.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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+    {file = "grpcio-1.78.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b0c689c02947d636bc7fab3e30cc3a3445cca99c834dfb77cd4a6cabfc1c5597"},
+    {file = "grpcio-1.78.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ce7599575eeb25c0f4dc1be59cada6219f3b56176f799627f44088b21381a28a"},
+    {file = "grpcio-1.78.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:684083fd383e9dc04c794adb838d4faea08b291ce81f64ecd08e4577c7398adf"},
+    {file = "grpcio-1.78.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:ab399ef5e3cd2a721b1038a0f3021001f19c5ab279f145e1146bb0b9f1b2b12c"},
+    {file = "grpcio-1.78.0-cp39-cp39-win32.whl", hash = "sha256:f3d6379493e18ad4d39537a82371c5281e153e963cecb13f953ebac155756525"},
+    {file = "grpcio-1.78.0-cp39-cp39-win_amd64.whl", hash = "sha256:5361a0630a7fdb58a6a97638ab70e1dae2893c4d08d7aba64ded28bb9e7a29df"},
+    {file = "grpcio-1.78.0.tar.gz", hash = "sha256:7382b95189546f375c174f53a5fa873cef91c4b8005faa05cc5b3beea9c4f1c5"},
 ]
 
+[package.dependencies]
+typing-extensions = ">=4.12,<5.0"
+
 [package.extras]
-protobuf = ["grpcio-tools (>=1.73.0)"]
+protobuf = ["grpcio-tools (>=1.78.0)"]
 
 [[package]]
 name = "gymnasium"
@@ -1182,6 +1179,7 @@ description = "A standard API for reinforcement learning and a diverse set of re
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "gymnasium-0.28.1-py3-none-any.whl", hash = "sha256:7bc9a5bce1022f997d1dbc152fc91d1ac977bad9cc7794cdc25437010867cabf"},
     {file = "gymnasium-0.28.1.tar.gz", hash = "sha256:4c2c745808792c8f45c6e88ad0a5504774394e0c126f6e3db555e720d3da6f24"},
@@ -1190,7 +1188,6 @@ files = [
 [package.dependencies]
 cloudpickle = ">=1.2.0"
 farama-notifications = ">=0.0.1"
-importlib-metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
 jax-jumpy = ">=1.0.0"
 numpy = ">=1.21.0"
 typing-extensions = ">=4.3.0"
@@ -1215,6 +1212,7 @@ description = "Read and write HDF5 files from Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "h5py-3.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5540daee2b236d9569c950b417f13fd112d51d78b4c43012de05774908dff3f5"},
     {file = "h5py-3.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:10894c55d46df502d82a7a4ed38f9c3fdbcb93efb42e25d275193e093071fade"},
@@ -1249,14 +1247,15 @@ numpy = ">=1.19.3"
 
 [[package]]
 name = "idna"
-version = "3.10"
+version = "3.11"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
-    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
+    {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"},
+    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
 ]
 
 [package.extras]
@@ -1264,14 +1263,15 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "imageio"
-version = "2.37.0"
-description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+version = "2.37.3"
+description = "Read and write images and video across all major formats. Supports scientific and volumetric data."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "imageio-2.37.0-py3-none-any.whl", hash = "sha256:11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed"},
-    {file = "imageio-2.37.0.tar.gz", hash = "sha256:71b57b3669666272c818497aebba2b4c5f20d5b37c81720e5e1a56d59c492996"},
+    {file = "imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0"},
+    {file = "imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451"},
 ]
 
 [package.dependencies]
@@ -1279,14 +1279,14 @@ numpy = "*"
 pillow = ">=8.3.2"
 
 [package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "numpy (>2)", "pillow-heif", "psutil", "rawpy", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-build = ["wheel"]
+all-plugins = ["astropy", "av", "fsspec[http]", "imageio-ffmpeg", "numpy (>2)", "pillow-heif", "psutil", "rawpy", "tifffile"]
+all-plugins-pypy = ["fsspec[http]", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
 dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
 docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
 ffmpeg = ["imageio-ffmpeg", "psutil"]
 fits = ["astropy"]
-full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpy (>2)", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "rawpy", "sphinx (<6)", "tifffile", "wheel"]
+freeimage = ["fsspec[http]"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github,http]", "imageio-ffmpeg", "numpy (>2)", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "rawpy", "sphinx (<6)", "tifffile"]
 gdal = ["gdal"]
 itk = ["itk"]
 linting = ["black", "flake8"]
@@ -1297,115 +1297,86 @@ test = ["fsspec[github]", "pytest", "pytest-cov"]
 tifffile = ["tifffile"]
 
 [[package]]
-name = "importlib-metadata"
-version = "8.7.0"
-description = "Read metadata from Python packages"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version == \"3.9\""
-files = [
-    {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
-    {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
-]
-
-[package.dependencies]
-zipp = ">=3.20"
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-perf = ["ipython"]
-test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
-type = ["pytest-mypy"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.5.2"
-description = "Read resources from Python packages"
+name = "imageio-ffmpeg"
+version = "0.6.0"
+description = "FFMPEG wrapper for Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version == \"3.9\""
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
-    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-macosx_10_9_intel.macosx_10_9_x86_64.whl", hash = "sha256:9d2baaf867088508d4a3458e61eeb30e945c4ad8016025545f66c4b5aaef0a61"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:b1ae3173414b5fc5f538a726c4e48ea97edc0d2cdc11f103afee655c463fa742"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1d47bebd83d2c5fc770720d211855f208af8a596c82d17730aa51e815cdee6dc"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c7e46fcec401dd990405049d2e2f475e2b397779df2519b544b8aab515195282"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-win32.whl", hash = "sha256:196faa79366b4a82f95c0f4053191d2013f4714a715780f0ad2a68ff37483cc2"},
+    {file = "imageio_ffmpeg-0.6.0-py3-none-win_amd64.whl", hash = "sha256:02fa47c83703c37df6bfe4896aab339013f62bf02c5ebf2dce6da56af04ffc0a"},
+    {file = "imageio_ffmpeg-0.6.0.tar.gz", hash = "sha256:e2556bed8e005564a9f925bb7afa4002d82770d6b08825078b7697ab88ba1755"},
 ]
 
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
-type = ["pytest-mypy"]
-
 [[package]]
 name = "iniconfig"
-version = "2.1.0"
+version = "2.3.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
-    {file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"},
+    {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"},
+    {file = "iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730"},
 ]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.5"
+version = "7.2.0"
 description = "IPython Kernel for Jupyter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
-    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
+    {file = "ipykernel-7.2.0-py3-none-any.whl", hash = "sha256:3bbd4420d2b3cc105cbdf3756bfc04500b1e52f090a90716851f3916c62e1661"},
+    {file = "ipykernel-7.2.0.tar.gz", hash = "sha256:18ed160b6dee2cbb16e5f3575858bc19d8f1fe6046a9a680c708494ce31d909e"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+appnope = {version = ">=0.1.2", markers = "platform_system == \"Darwin\""}
 comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
-jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-client = ">=8.8.0"
+jupyter-core = ">=5.1,<6.0.dev0 || >=6.1.dev0"
 matplotlib-inline = ">=0.1"
-nest-asyncio = "*"
-packaging = "*"
-psutil = "*"
-pyzmq = ">=24"
-tornado = ">=6.1"
+nest-asyncio = ">=1.4"
+packaging = ">=22"
+psutil = ">=5.7"
+pyzmq = ">=25"
+tornado = ">=6.4.1"
 traitlets = ">=5.4.0"
 
 [package.extras]
-cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+cov = ["coverage[toml]", "matplotlib", "pytest-cov", "trio"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx (<8.2.0)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0,<10)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipyparallel"
-version = "9.0.1"
+version = "9.1.0"
 description = "Interactive Parallel Computing with IPython"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "ipyparallel-9.0.1-py3-none-any.whl", hash = "sha256:cc0702e26af416a1c35c73eb6dbc3a6a2c49417cb24dda4511be85c9fe87ea64"},
-    {file = "ipyparallel-9.0.1.tar.gz", hash = "sha256:2e592cad2200c5a94fbbff639bff36e6ec9122f34b36b2fc6b4d678d9e98f29c"},
+    {file = "ipyparallel-9.1.0-py3-none-any.whl", hash = "sha256:dff90d758b6f999e5803306cd1900e15029655b475d780989d8b2bf338730213"},
+    {file = "ipyparallel-9.1.0.tar.gz", hash = "sha256:ecc81a1bfd2681eb571e361839d5defcbeec583ae3ee0503bc83b066106b88cd"},
 ]
 
 [package.dependencies]
 decorator = "*"
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 ipykernel = ">=6.9.1"
 ipython = ">=5"
 jupyter-client = ">=7"
@@ -1426,15 +1397,15 @@ test = ["ipython[test]", "pytest", "pytest-asyncio", "pytest-cov", "testpath"]
 
 [[package]]
 name = "ipython"
-version = "8.18.1"
+version = "8.38.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\" and (sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\")"
 files = [
-    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
-    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
+    {file = "ipython-8.38.0-py3-none-any.whl", hash = "sha256:750162629d800ac65bb3b543a14e7a74b0e88063eac9b92124d4b2aa3f6d8e86"},
+    {file = "ipython-8.38.0.tar.gz", hash = "sha256:9cfea8c903ce0867cc2f23199ed8545eb741f3a69420bfcf3743ad1cec856d39"},
 ]
 
 [package.dependencies]
@@ -1443,59 +1414,60 @@ decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-prompt-toolkit = ">=3.0.41,<3.1.0"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
-stack-data = "*"
-traitlets = ">=5"
-typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+stack_data = "*"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli ; python_version < \"3.11\"", "typing_extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "ipython"
-version = "9.2.0"
+version = "9.10.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and python_version >= \"3.11\""
 files = [
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-    {file = "ipython-9.2.0.tar.gz", hash = "sha256:62a9373dbc12f28f9feaf4700d052195bf89806279fc8ca11f3f54017d04751b"},
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 [package.dependencies]
-colorama = {version = "*", markers = "sys_platform == \"win32\""}
-decorator = "*"
-ipython-pygments-lexers = "*"
-jedi = ">=0.16"
-matplotlib-inline = "*"
+colorama = {version = ">=0.4.4", markers = "sys_platform == \"win32\""}
+decorator = ">=4.3.2"
+ipython-pygments-lexers = ">=1.0.0"
+jedi = ">=0.18.1"
+matplotlib-inline = ">=0.1.5"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
 prompt_toolkit = ">=3.0.41,<3.1.0"
-pygments = ">=2.4.0"
-stack_data = "*"
+pygments = ">=2.11.0"
+stack_data = ">=0.6.0"
 traitlets = ">=5.13.0"
 typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["ipython[doc,matplotlib,test,test-extra]"]
+all = ["argcomplete (>=3.0)", "ipython[doc,matplotlib,terminal,test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinx_toml (==0.0.4)", "typing_extensions"]
-matplotlib = ["matplotlib"]
-test = ["packaging", "pytest", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "ipykernel", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbclient", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[matplotlib,test]", "setuptools (>=70.0)", "sphinx (>=8.0)", "sphinx-rtd-theme (>=0.1.8)", "sphinx_toml (==0.0.4)", "typing_extensions"]
+matplotlib = ["matplotlib (>3.9)"]
+test = ["packaging (>=20.1.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=1.0.0)", "setuptools (>=61.2)", "testpath (>=0.2)"]
+test-extra = ["curio", "ipykernel (>6.30)", "ipython[matplotlib]", "ipython[test]", "jupyter_ai", "nbclient", "nbformat", "numpy (>=1.27)", "pandas (>2.1)", "trio (>=0.1.0)"]
 
 [[package]]
 name = "ipython-pygments-lexers"
@@ -1504,7 +1476,7 @@ description = "Defines a variety of Pygments lexers for highlighting IPython cod
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and python_version >= \"3.11\""
 files = [
     {file = "ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c"},
     {file = "ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81"},
@@ -1520,6 +1492,7 @@ description = "Common backend for Jax or Numpy."
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "jax-jumpy-1.0.0.tar.gz", hash = "sha256:195fb955cc4c2b7f0b1453e3cb1fb1c414a51a407ffac7a51e69a73cb30d59ad"},
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@@ -1539,6 +1512,7 @@ description = "An autocompletion tool for Python that can be used for text edito
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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     {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
@@ -1559,6 +1533,7 @@ description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
     {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
@@ -1572,502 +1547,372 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.4.2"
+version = "1.5.3"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713"},
+    {file = "joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3"},
 ]
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.3"
+version = "8.8.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.dependencies]
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-jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-core = ">=5.1"
 python-dateutil = ">=2.8.2"
-pyzmq = ">=23.0"
-tornado = ">=6.2"
+pyzmq = ">=25.0"
+tornado = ">=6.4.1"
 traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
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+orjson = ["orjson"]
+test = ["anyio", "coverage", "ipykernel (>=6.14)", "msgpack", "mypy ; platform_python_implementation != \"PyPy\"", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.6.2)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.2"
+version = "5.9.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 [package.dependencies]
 platformdirs = ">=2.5"
-pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = ">=5.3"
 
 [package.extras]
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-test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "kiwisolver"
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 ]
 
 [package.dependencies]
@@ -2078,7 +1923,7 @@ kiwisolver = ">=1.3.1"
 numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
-pyparsing = ">=2.3.1"
+pyparsing = ">=3"
 python-dateutil = ">=2.7"
 
 [package.extras]
@@ -2086,30 +1931,22 @@ dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setup
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.7"
+version = "0.2.1"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe"},
 ]
 
 [package.dependencies]
 traitlets = "*"
 
-[[package]]
-name = "mdurl"
-version = "0.1.2"
-description = "Markdown URL utilities"
-optional = false
-python-versions = ">=3.7"
-groups = ["main"]
-files = [
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-]
+[package.extras]
+test = ["flake8", "nbdime", "nbval", "notebook", "pytest"]
 
 [[package]]
 name = "mo-gymnasium"
@@ -2118,6 +1955,7 @@ description = "A standard API for MORL and a diverse set of reference environmen
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2146,6 +1984,7 @@ description = "A standard API for Multi-Objective Multi-Agent Decision making an
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2168,6 +2007,37 @@ all = ["chex (>=0.1)", "distrax (>=0.1.3)", "etils (>=1.3)", "flax (>=0.6)", "ja
 learning = ["chex (>=0.1)", "distrax (>=0.1.3)", "etils (>=1.3)", "flax (>=0.6)", "jax (>=0.4.13)", "matplotlib (>=3.7.4)", "morl-baselines[all]", "optax (>=0.1)", "orbax-checkpoint (>=0.2.3)", "pandas (>=2.0.3)", "supersuit (>=3.9)", "tqdm (>=4.66.1)"]
 testing = ["pytest (==7.1.3)"]
 
+[[package]]
+name = "moocore"
+version = "0.2.0"
+description = "Core Algorithms for Multi-Objective Optimization"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
+files = [
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+]
+
+[package.dependencies]
+cffi = ">=1.17.1"
+numpy = ">=1.24"
+platformdirs = "*"
+
+[package.extras]
+benchmarks = ["botorch", "desdeo", "fast-pareto", "jmetalpy", "matplotlib", "nevergrad", "numba", "numpy", "optuna", "pandas (>=2)", "paretoset", "py-cpuinfo", "pymoo", "seqme"]
+coverage = ["coverage[toml]", "gcovr"]
+dev = ["pre-commit (>=3.3.2)", "ruff (>=0.11.2)", "tox (>=4.6.2)"]
+docs = ["ipykernel", "ipywidgets", "jupyter", "jupyterlab", "kaleido", "pandas (>=2)", "plotly", "pydata-sphinx-theme (>=0.16)", "seaborn", "sphinx (>=6)", "sphinx-autodoc-typehints", "sphinx-copybutton", "sphinx-design", "sphinx-gallery (>=0.19)", "sphinxcontrib-bibtex", "sphinxcontrib-napoleon"]
+test = ["pandas (>=2)", "pytest (>7)"]
+
 [[package]]
 name = "morl-baselines"
 version = "1.0.0"
@@ -2175,6 +2045,7 @@ description = "Implementations of multi-objective reinforcement learning (MORL)
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2203,21 +2074,29 @@ testing = ["pytest (==7.1.3)"]
 
 [[package]]
 name = "moviepy"
-version = "0.2.3.1"
+version = "1.0.3"
 description = "Video editing with Python"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "moviepy-1.0.3.tar.gz", hash = "sha256:2884e35d1788077db3ff89e763c5ba7bfddbd7ae9108c9bc809e7ba58fa433f5"},
 ]
 
 [package.dependencies]
-decorator = "*"
-imageio = "*"
-numpy = "*"
-tqdm = "*"
+decorator = ">=4.0.2,<5.0"
+imageio = {version = ">=2.5,<3.0", markers = "python_version >= \"3.4\""}
+imageio_ffmpeg = {version = ">=0.2.0", markers = "python_version >= \"3.4\""}
+numpy = {version = ">=1.17.3", markers = "python_version > \"2.7\""}
+proglog = "<=1.0.0"
+requests = ">=2.8.1,<3.0"
+tqdm = ">=4.11.2,<5.0"
+
+[package.extras]
+doc = ["Sphinx (>=1.5.2,<2.0)", "numpydoc (>=0.6.0,<1.0)", "pygame (>=1.9.3,<2.0) ; python_version < \"3.8\"", "sphinx_rtd_theme (>=0.1.10b0,<1.0)"]
+optional = ["matplotlib (>=2.0.0,<3.0) ; python_version >= \"3.4\"", "opencv-python (>=3.0,<4.0) ; python_version != \"2.7\"", "scikit-image (>=0.13.0,<1.0) ; python_version >= \"3.4\"", "scikit-learn ; python_version >= \"3.4\"", "scipy (>=0.19.0,<1.5) ; python_version != \"3.3\"", "youtube_dl"]
+test = ["coverage (<5.0)", "coveralls (>=1.1,<2.0)", "pytest (>=3.0.0,<4.0)", "pytest-cov (>=2.5.1,<3.0)", "requests (>=2.8.1,<3.0)"]
 
 [[package]]
 name = "mpmath"
@@ -2226,6 +2105,7 @@ description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2239,57 +2119,60 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "multiprocess"
-version = "0.70.17"
+version = "0.70.19"
 description = "better multiprocessing and multithreading in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
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+dill = ">=0.4.1"
 
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 description = "Extremely lightweight compatibility layer between dataframe libraries"
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 [package.extras]
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 dask = ["dask[dataframe] (>=2024.8)"]
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 modin = ["modin"]
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-polars = ["polars (>=0.20.3)"]
-pyarrow = ["pyarrow (>=11.0.0)"]
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 pyspark = ["pyspark (>=3.5.0)"]
 pyspark-connect = ["pyspark[connect] (>=3.5.0)"]
-sqlframe = ["sqlframe (>=3.22.0)"]
+sql = ["duckdb (>=1.1)", "sqlparse"]
+sqlframe = ["sqlframe (>=3.22.0,!=3.39.3)"]
 
 [[package]]
 name = "nest-asyncio"
@@ -2298,6 +2181,7 @@ description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2305,43 +2189,70 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.2.1"
+version = "3.4.2"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\" and (sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\")"
 files = [
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 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "networkx"
-version = "3.5"
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 description = "Python package for creating and manipulating graphs and networks"
 optional = false
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
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+
+[[package]]
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+python-versions = "!=3.14.1,>=3.11"
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 [package.extras]
+benchmarking = ["asv", "virtualenv"]
 default = ["matplotlib (>=3.8)", "numpy (>=1.25)", "pandas (>=2.0)", "scipy (>=1.11.2)"]
 developer = ["mypy (>=1.15)", "pre-commit (>=4.1)"]
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 extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+release = ["build (>=0.10)", "changelist (==0.5)", "twine (>=4.0)", "wheel (>=0.40)"]
 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)", "pytest-xdist (>=3.0)"]
 test-extras = ["pytest-mpl", "pytest-randomly"]
 
@@ -2352,6 +2263,7 @@ description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -2390,7 +2302,7 @@ description = "CUBLAS native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2403,7 +2315,7 @@ description = "CUDA profiling tools runtime libs."
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2416,7 +2328,7 @@ description = "NVRTC native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2429,7 +2341,7 @@ description = "CUDA Runtime native Libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2442,7 +2354,7 @@ description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2458,7 +2370,7 @@ description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2471,7 +2383,7 @@ description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
 files = [
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@@ -2484,7 +2396,7 @@ description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_machine == \"x86_64\" and platform_system == \"Linux\""
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 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2600,14 +2524,15 @@ mkl = ["osqp-mkl"]
 
 [[package]]
 name = "packaging"
-version = "24.2"
+version = "26.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [[package]]
@@ -2617,7 +2542,7 @@ description = "Powerful data structures for data analysis, time series, and stat
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version >= \"3.12\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and python_version >= \"3.12\""
 files = [
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@@ -2677,55 +2602,68 @@ xml = ["lxml (>=4.6.3)"]
 
 [[package]]
 name = "pandas"
-version = "2.2.3"
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 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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@@ -2764,30 +2702,32 @@ xml = ["lxml (>=4.9.2)"]
 
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-version = "0.8.4"
+version = "0.8.6"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
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+qa = ["flake8 (==5.0.4)", "types-setuptools (==67.2.0.1)", "zuban (==0.5.1)"]
 testing = ["docopt", "pytest"]
 
 [[package]]
 name = "patsy"
-version = "1.0.1"
+version = "1.0.2"
 description = "A Python package for describing statistical models and for building design matrices."
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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 [package.dependencies]
@@ -2803,6 +2743,7 @@ description = "Gymnasium for multi-agent reinforcement learning."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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 python-versions = "*"
 groups = ["main"]
-markers = "(python_version < \"3.11\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and sys_platform != \"win32\""
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
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 mic = ["olefile"]
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-typing = ["typing-extensions ; python_version < \"3.10\""]
+test-arrow = ["arro3-compute", "arro3-core", "nanoarrow", "pyarrow"]
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 xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.3.7"
+version = "4.9.4"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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-[package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.4)", "pytest-cov (>=6)", "pytest-mock (>=3.14)"]
-type = ["mypy (>=1.14.1)"]
-
 [[package]]
 name = "platypus-opt"
-version = "1.1.0"
+version = "1.4.1"
 description = "Multiobjective optimization in Python"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "platypus_opt-1.4.1.tar.gz", hash = "sha256:b341790be67354c2c964bb3431b9acc8a1a147aca9d9464a43720dc58b3895e8"},
 ]
 
-[package.dependencies]
-numpy = "*"
-
 [package.extras]
-test = ["mock", "pytest"]
+docs = ["sphinx", "sphinx-rtd-theme"]
+full = ["Platypus-Opt[docs]", "Platypus-Opt[test]", "mpi4py"]
+test = ["flake8", "flake8-pyproject", "jsonpickle", "matplotlib", "mock", "numpy", "pytest"]
 
 [[package]]
 name = "plotly"
-version = "6.0.1"
+version = "6.6.0"
 description = "An open-source interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "plotly-6.6.0-py3-none-any.whl", hash = "sha256:8d6daf0f87412e0c0bfe72e809d615217ab57cc715899a1e5145135a7800d1d0"},
+    {file = "plotly-6.6.0.tar.gz", hash = "sha256:b897f15f3b02028d69f755f236be890ba950d0a42d7dfc619b44e2d8cea8748c"},
 ]
 
 [package.dependencies]
@@ -2983,7 +2942,12 @@ narwhals = ">=1.15.1"
 packaging = "*"
 
 [package.extras]
+dev = ["plotly[dev-optional]"]
+dev-build = ["build", "jupyter", "plotly[dev-core]"]
+dev-core = ["pytest", "requests", "ruff (==0.11.12)"]
+dev-optional = ["anywidget", "colorcet", "fiona (<=1.9.6) ; python_version <= \"3.8\"", "geopandas", "inflect", "numpy", "orjson", "pandas", "pdfrw", "pillow", "plotly-geo", "plotly[dev-build]", "plotly[kaleido]", "polars[timezone]", "pyarrow", "pyshp", "pytz", "scikit-image", "scipy", "shapely", "statsmodels", "vaex ; python_version <= \"3.9\"", "xarray"]
 express = ["numpy"]
+kaleido = ["kaleido (>=1.1.0)"]
 
 [[package]]
 name = "pluggy"
@@ -2992,6 +2956,7 @@ description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -3003,14 +2968,15 @@ testing = ["coverage", "pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pooch"
-version = "1.8.2"
+version = "1.9.0"
 description = "A friend to fetch your data files"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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+    {file = "pooch-1.9.0.tar.gz", hash = "sha256:de46729579b9857ffd3e741987a2f6d5e0e03219892c167c6578c0091fb511ed"},
 ]
 
 [package.dependencies]
@@ -3021,18 +2987,36 @@ requests = ">=2.19.0"
 [package.extras]
 progress = ["tqdm (>=4.41.0,<5.0.0)"]
 sftp = ["paramiko (>=2.7.0)"]
+test = ["pytest-httpserver", "pytest-localftpserver"]
 xxhash = ["xxhash (>=1.4.3)"]
 
+[[package]]
+name = "proglog"
+version = "0.1.12"
+description = "Log and progress bar manager for console, notebooks, web..."
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
+files = [
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+    {file = "proglog-0.1.12.tar.gz", hash = "sha256:361ee074721c277b89b75c061336cb8c5f287c92b043efa562ccf7866cda931c"},
+]
+
+[package.dependencies]
+tqdm = "*"
+
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.51"
+version = "3.0.52"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.dependencies]
@@ -3040,46 +3024,60 @@ wcwidth = "*"
 
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 name = "protobuf"
-version = "6.31.1"
+version = "6.33.6"
 description = ""
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 python-versions = ">=3.9"
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 [[package]]
 name = "psutil"
-version = "7.0.0"
-description = "Cross-platform lib for process and system monitoring in Python.  NOTE: the syntax of this script MUST be kept compatible with Python 2.7."
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-test = ["pytest", "pytest-xdist", "setuptools"]
+dev = ["abi3audit", "black", "check-manifest", "colorama ; os_name == \"nt\"", "coverage", "packaging", "psleak", "pylint", "pyperf", "pypinfo", "pyreadline3 ; os_name == \"nt\"", "pytest", "pytest-cov", "pytest-instafail", "pytest-xdist", "pywin32 ; os_name == \"nt\" and implementation_name != \"pypy\"", "requests", "rstcheck", "ruff", "setuptools", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "validate-pyproject[all]", "virtualenv", "vulture", "wheel", "wheel ; os_name == \"nt\" and implementation_name != \"pypy\"", "wmi ; os_name == \"nt\" and implementation_name != \"pypy\""]
+test = ["psleak", "pytest", "pytest-instafail", "pytest-xdist", "pywin32 ; os_name == \"nt\" and implementation_name != \"pypy\"", "setuptools", "wheel ; os_name == \"nt\" and implementation_name != \"pypy\"", "wmi ; os_name == \"nt\" and implementation_name != \"pypy\""]
 
 [[package]]
 name = "ptyprocess"
@@ -3088,7 +3086,7 @@ description = "Run a subprocess in a pseudo terminal"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "(python_version < \"3.11\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and sys_platform != \"win32\""
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -3101,6 +3099,7 @@ description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -3111,45 +3110,48 @@ tests = ["pytest"]
 
 [[package]]
 name = "pycountry"
-version = "24.6.1"
+version = "26.2.16"
 description = "ISO country, subdivision, language, currency and script definitions and their translations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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+    {file = "pycountry-26.2.16.tar.gz", hash = "sha256:5b6027d453fcd6060112b951dd010f01f168b51b4bf8a1f1fc8c95c8d94a0801"},
 ]
 
 [[package]]
 name = "pycparser"
-version = "2.22"
+version = "3.0"
 description = "C parser in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main"]
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and implementation_name != \"PyPy\""
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 ]
 
 [[package]]
 name = "pydantic"
-version = "2.11.7"
+version = "2.12.5"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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 annotated-types = ">=0.6.0"
-pydantic-core = "2.33.2"
-typing-extensions = ">=4.12.2"
-typing-inspection = ">=0.4.0"
+pydantic-core = "2.41.5"
+typing-extensions = ">=4.14.1"
+typing-inspection = ">=0.4.2"
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
@@ -3157,115 +3159,138 @@ timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows
 
 [[package]]
 name = "pydantic-core"
-version = "2.33.2"
+version = "2.41.5"
 description = "Core functionality for Pydantic validation and serialization"
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 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
-alive-progress = "*"
+alive_progress = "*"
 autograd = ">=1.4"
 cma = ">=3.2.2"
 Deprecated = "*"
-dill = "*"
 matplotlib = ">=3"
+moocore = ">=0.1.7"
 numpy = ">=1.19.3"
 scipy = ">=1.1"
 
+[package.extras]
+dev = ["ipykernel", "ipython", "jupyter", "nbformat", "numba", "optproblems", "pandas", "pyrecorder", "pytest"]
+full = ["numba", "pandas", "pymoo[others]", "pymoo[parallelization]", "scikit-learn"]
+others = ["optuna"]
+parallelization = ["dask[distributed]", "joblib", "ray[default]"]
+
 [[package]]
 name = "pymunk"
 version = "6.2.0"
@@ -3413,6 +3457,7 @@ description = "Pymunk is a easy-to-use pythonic 2d physics library"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
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 files = [
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@@ -3462,6 +3507,7 @@ description = "Standard OpenGL bindings for Python"
 optional = false
 python-versions = "*"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -3470,79 +3516,67 @@ files = [
 
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 name = "pyopengl-accelerate"
-version = "3.1.9"
+version = "3.1.10"
 description = "Cython-coded accelerators for PyOpenGL"
 optional = false
 python-versions = "*"
 groups = ["main"]
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 [[package]]
 name = "pyparsing"
-version = "3.2.3"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+version = "3.3.2"
+description = "pyparsing - Classes and methods to define and execute parsing grammars"
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 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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 ]
 
 [package.extras]
@@ -3550,21 +3584,22 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
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-version = "8.4.1"
+version = "9.0.2"
 description = "pytest: simple powerful testing with Python"
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+python-versions = ">=3.10"
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 [package.dependencies]
 colorama = {version = ">=0.4", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1", markers = "python_version < \"3.11\""}
-iniconfig = ">=1"
-packaging = ">=20"
+iniconfig = ">=1.0.1"
+packaging = ">=22"
 pluggy = ">=1.5,<2"
 pygments = ">=2.7.2"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
@@ -3579,6 +3614,7 @@ description = "Open API to/fro routes, models, and tests. Convert between docstr
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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@@ -3594,6 +3630,7 @@ description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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@@ -3812,19 +3844,20 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
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 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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@@ -4102,6 +4134,7 @@ description = "Statistical data visualization"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
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 fastapi = ["fastapi (>=0.79.0)"]
 flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
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 http2 = ["httpcore[http2] (==1.*)"]
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 huey = ["huey (>=2)"]
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 langchain = ["langchain (>=0.0.210)"]
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 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
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 [package.extras]
@@ -4286,39 +4327,25 @@ test = ["pytest"]
 
 [[package]]
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-
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-[package.extras]
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 [[package]]
 name = "six"
@@ -4327,6 +4354,7 @@ description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 groups = ["main"]
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+version = "5.0.3"
 description = "A pure Python implementation of a sliding window memory map manager"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
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 ]
 
 [[package]]
@@ -4351,6 +4380,7 @@ description = "Extract data from python stack frames and tracebacks for informat
 optional = false
 python-versions = "*"
 groups = ["main"]
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+version = "0.14.6"
 description = "Statistical computations and models for Python"
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 [package.dependencies]
@@ -4413,8 +4450,8 @@ scipy = ">=1.8,<1.9.2 || >1.9.2"
 
 [package.extras]
 build = ["cython (>=3.0.10)"]
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 [[package]]
 name = "supersuit"
@@ -4423,6 +4460,7 @@ description = "Wrappers for Gymnasium and PettingZoo"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
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 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
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 python-versions = ">=3.9"
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+    {file = "tomli-2.4.0-cp314-cp314t-win_amd64.whl", hash = "sha256:2add28aacc7425117ff6364fe9e06a183bb0251b03f986df0e78e974047571fd"},
+    {file = "tomli-2.4.0-cp314-cp314t-win_arm64.whl", hash = "sha256:2b1e3b80e1d5e52e40e9b924ec43d81570f0e7d09d11081b797bc4692765a3d4"},
+    {file = "tomli-2.4.0-py3-none-any.whl", hash = "sha256:1f776e7d669ebceb01dee46484485f43a4048746235e683bcdffacdf1fb4785a"},
+    {file = "tomli-2.4.0.tar.gz", hash = "sha256:aa89c3f6c277dd275d8e243ad24f3b5e701491a860d5121f2cdd399fbb31fc9c"},
 ]
 
 [[package]]
@@ -4596,6 +4655,7 @@ description = "Tensors and Dynamic neural networks in Python with strong GPU acc
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "torch-2.4.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:362f82e23a4cd46341daabb76fba08f04cd646df9bfaf5da50af97cb60ca4971"},
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@@ -4646,35 +4706,36 @@ optree = ["optree (>=0.11.0)"]
 
 [[package]]
 name = "tornado"
-version = "6.4.2"
+version = "6.5.5"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
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-    {file = "tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b"},
+    {file = "tornado-6.5.5-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:487dc9cc380e29f58c7ab88f9e27cdeef04b2140862e5076a66fb6bb68bb1bfa"},
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+    {file = "tornado-6.5.5-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:435319e9e340276428bbdb4e7fa732c2d399386d1de5686cb331ec8eee754f07"},
+    {file = "tornado-6.5.5-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3f54aa540bdbfee7b9eb268ead60e7d199de5021facd276819c193c0fb28ea4e"},
+    {file = "tornado-6.5.5-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:36abed1754faeb80fbd6e64db2758091e1320f6bba74a4cf8c09cd18ccce8aca"},
+    {file = "tornado-6.5.5-cp39-abi3-win32.whl", hash = "sha256:dd3eafaaeec1c7f2f8fdcd5f964e8907ad788fe8a5a32c4426fbbdda621223b7"},
+    {file = "tornado-6.5.5-cp39-abi3-win_amd64.whl", hash = "sha256:6443a794ba961a9f619b1ae926a2e900ac20c34483eea67be4ed8f1e58d3ef7b"},
+    {file = "tornado-6.5.5-cp39-abi3-win_arm64.whl", hash = "sha256:2c9a876e094109333f888539ddb2de4361743e5d21eece20688e3e351e4990a6"},
+    {file = "tornado-6.5.5.tar.gz", hash = "sha256:192b8f3ea91bd7f1f50c06955416ed76c6b72f96779b962f07f911b91e8d30e9"},
 ]
 
 [[package]]
 name = "tqdm"
-version = "4.67.1"
+version = "4.67.3"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
-    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
+    {file = "tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf"},
+    {file = "tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb"},
 ]
 
 [package.dependencies]
@@ -4694,6 +4755,7 @@ description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
     {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
@@ -4710,7 +4772,7 @@ description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""
+markers = "python_version < \"3.13\" and (sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
     {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
     {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
@@ -4729,46 +4791,44 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "typeguard"
-version = "4.4.2"
+version = "4.5.1"
 description = "Run-time type checker for Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "typeguard-4.4.2-py3-none-any.whl", hash = "sha256:77a78f11f09777aeae7fa08585f33b5f4ef0e7335af40005b0c422ed398ff48c"},
-    {file = "typeguard-4.4.2.tar.gz", hash = "sha256:a6f1065813e32ef365bc3b3f503af8a96f9dd4e0033a02c28c4a4983de8c6c49"},
+    {file = "typeguard-4.5.1-py3-none-any.whl", hash = "sha256:44d2bf329d49a244110a090b55f5f91aa82d9a9834ebfd30bcc73651e4a8cc40"},
+    {file = "typeguard-4.5.1.tar.gz", hash = "sha256:f6f8ecbbc819c9bc749983cc67c02391e16a9b43b8b27f15dc70ed7c4a007274"},
 ]
 
 [package.dependencies]
-importlib_metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
-typing_extensions = ">=4.10.0"
-
-[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.3.0)"]
-test = ["coverage[toml] (>=7)", "mypy (>=1.2.0) ; platform_python_implementation != \"PyPy\"", "pytest (>=7)"]
+typing_extensions = ">=4.14.0"
 
 [[package]]
 name = "typing-extensions"
-version = "4.13.2"
-description = "Backported and Experimental Type Hints for Python 3.8+"
+version = "4.15.0"
+description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
-    {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
+    {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"},
+    {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
 ]
 
 [[package]]
 name = "typing-inspection"
-version = "0.4.1"
+version = "0.4.2"
 description = "Runtime typing introspection tools"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51"},
-    {file = "typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28"},
+    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
+    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
 ]
 
 [package.dependencies]
@@ -4776,58 +4836,57 @@ typing-extensions = ">=4.12.0"
 
 [[package]]
 name = "tyro"
-version = "0.9.24"
+version = "1.0.10"
 description = "CLI interfaces & config objects, from types"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "tyro-0.9.24-py3-none-any.whl", hash = "sha256:d8152e47375419752210da455226007b4bb9bd9c65af1de8fb12daf0658c91dc"},
-    {file = "tyro-0.9.24.tar.gz", hash = "sha256:5a9ef93d1b8e93cff2c5d82789a571d905d152e92af82a3ec96a17d668194df3"},
+    {file = "tyro-1.0.10-py3-none-any.whl", hash = "sha256:8de87a3a40c8a91f10831f8f0638cd0eed00f0e4de9cd3d561e967f407477210"},
+    {file = "tyro-1.0.10.tar.gz", hash = "sha256:2822eacac963a4922bf7eafe3b156a1f0f7fe8e34148202987581224f25565c2"},
 ]
 
 [package.dependencies]
-colorama = {version = ">=0.4.0", markers = "platform_system == \"Windows\""}
 docstring-parser = ">=0.15"
-eval-type-backport = {version = ">=0.1.3", markers = "python_version < \"3.10\""}
-rich = ">=11.1.0"
-shtab = ">=1.5.6"
 typeguard = ">=4.0.0"
 typing-extensions = ">=4.13.0"
 
 [package.extras]
-dev = ["attrs (>=21.4.0)", "coverage[toml] (>=6.5.0)", "eval-type-backport (>=0.1.3)", "ml-collections (>=0.1.0)", "msgspec (>=0.18.6)", "mypy (>=1.4.1)", "omegaconf (>=2.2.2)", "pydantic (>=2.5.2,!=2.10.0)", "pyright (>=1.1.349,!=1.1.379)", "pytest (>=7.1.2)", "pytest-cov (>=3.0.0)", "pytest-xdist (>=3.5.0)", "pyyaml (>=6.0)", "ruff (>=0.1.13)"]
-dev-nn = ["attrs (>=21.4.0)", "coverage[toml] (>=6.5.0)", "eval-type-backport (>=0.1.3)", "flax (>=0.6.9) ; python_version <= \"3.12\"", "ml-collections (>=0.1.0)", "msgspec (>=0.18.6)", "mypy (>=1.4.1)", "numpy (>=1.20.0)", "omegaconf (>=2.2.2)", "pydantic (>=2.5.2,!=2.10.0)", "pyright (>=1.1.349,!=1.1.379)", "pytest (>=7.1.2)", "pytest-cov (>=3.0.0)", "pytest-xdist (>=3.5.0)", "pyyaml (>=6.0)", "ruff (>=0.1.13)", "torch (>=1.10.0) ; python_version <= \"3.12\""]
+dev = ["attrs (>=21.4.0)", "coverage[toml] (>=6.5.0)", "eval-type-backport (>=0.1.3)", "ml-collections (>=0.1.0)", "msgspec (>=0.18.6)", "mypy (>=1.4.1)", "omegaconf (>=2.2.2)", "pydantic (>=2.5.2,!=2.10.0)", "pyright (>=1.1.349,!=1.1.379)", "pytest (>=7.1.2)", "pytest-cov (>=3.0.0)", "pytest-xdist (>=3.5.0)", "pyyaml (>=6.0)", "shtab (>=1.5.6)", "universal-pathlib (>=0.2.0)"]
+dev-nn = ["attrs (>=21.4.0)", "coverage[toml] (>=6.5.0)", "eval-type-backport (>=0.1.3)", "flax (>=0.6.9) ; python_version >= \"3.10\" and python_version <= \"3.13\"", "ml-collections (>=0.1.0)", "msgspec (>=0.18.6)", "mypy (>=1.4.1)", "numpy (>=1.20.0)", "omegaconf (>=2.2.2)", "pydantic (>=2.5.2,!=2.10.0)", "pyright (>=1.1.349,!=1.1.379)", "pytest (>=7.1.2)", "pytest-cov (>=3.0.0)", "pytest-xdist (>=3.5.0)", "pyyaml (>=6.0)", "shtab (>=1.5.6)", "torch (>=1.10.0) ; python_version >= \"3.9\" and python_version <= \"3.13\"", "universal-pathlib (>=0.2.0)"]
 
 [[package]]
 name = "tzdata"
-version = "2025.2"
+version = "2025.3"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
-    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
+    {file = "tzdata-2025.3-py2.py3-none-any.whl", hash = "sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1"},
+    {file = "tzdata-2025.3.tar.gz", hash = "sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7"},
 ]
 
 [[package]]
 name = "urllib3"
-version = "2.3.0"
+version = "2.6.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
-    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
-    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
+    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
+    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+brotli = ["brotli (>=1.2.0) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=1.2.0.0) ; platform_python_implementation != \"CPython\""]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
+zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""]
 
 [[package]]
 name = "wandb"
@@ -4836,6 +4895,7 @@ description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
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 pyyaml = "*"
@@ -4883,123 +4939,140 @@ workspaces = ["wandb-workspaces"]
 
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+version = "0.6.0"
 description = "Measures the displayed width of unicode strings in a terminal"
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 [package.extras]
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+    {file = "wrapt-2.1.2-cp39-cp39-musllinux_1_2_riscv64.whl", hash = "sha256:478282ebd3795a089154fb16d3db360e103aa13d3b2ad30f8f6aac0d2207de0e"},
+    {file = "wrapt-2.1.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:3756219045f73fb28c5d7662778e4156fbd06cf823c4d2d4b19f97305e52819c"},
+    {file = "wrapt-2.1.2-cp39-cp39-win32.whl", hash = "sha256:b8aefb4dbb18d904b96827435a763fa42fc1f08ea096a391710407a60983ced8"},
+    {file = "wrapt-2.1.2-cp39-cp39-win_amd64.whl", hash = "sha256:e5aeab8fe15c3dff75cfee94260dcd9cded012d4ff06add036c28fae7718593b"},
+    {file = "wrapt-2.1.2-cp39-cp39-win_arm64.whl", hash = "sha256:f069e113743a21a3defac6677f000068ebb931639f789b5b226598e247a4c89e"},
+    {file = "wrapt-2.1.2-py3-none-any.whl", hash = "sha256:b8fd6fa2b2c4e7621808f8c62e8317f4aae56e59721ad933bac5239d913cf0e8"},
+    {file = "wrapt-2.1.2.tar.gz", hash = "sha256:3996a67eecc2c68fd47b4e3c564405a5777367adfd9b8abb58387b63ee83b21e"},
 ]
 
+[package.extras]
+dev = ["pytest", "setuptools"]
+
 [[package]]
 name = "xarray"
 version = "2024.7.0"
@@ -5007,7 +5080,7 @@ description = "N-D labeled arrays and datasets in Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\" or python_version >= \"3.12\""
+markers = "(sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\") and python_version >= \"3.12\""
 files = [
     {file = "xarray-2024.7.0-py3-none-any.whl", hash = "sha256:1b0fd51ec408474aa1f4a355d75c00cc1c02bd425d97b2c2e551fd21810e7f64"},
     {file = "xarray-2024.7.0.tar.gz", hash = "sha256:4cae512d121a8522d41e66d942fb06c526bc1fd32c2c181d5fe62fe65b671638"},
@@ -5028,15 +5101,15 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "xarray"
-version = "2025.3.1"
+version = "2025.6.1"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version == \"3.11\""
+markers = "python_version < \"3.12\" and (sys_platform == \"win32\" or sys_platform == \"emscripten\" or sys_platform != \"win32\" and sys_platform != \"emscripten\")"
 files = [
-    {file = "xarray-2025.3.1-py3-none-any.whl", hash = "sha256:3404e313930c226db70a945377441ea3c957225d8ba2d429e764c099bb91a546"},
-    {file = "xarray-2025.3.1.tar.gz", hash = "sha256:0252c96a73528b29d1ed7f0ab28d928d2ec00ad809e47369803b184dece1e447"},
+    {file = "xarray-2025.6.1-py3-none-any.whl", hash = "sha256:8b988b47f67a383bdc3b04c5db475cd165e580134c1f1943d52aee4a9c97651b"},
+    {file = "xarray-2025.6.1.tar.gz", hash = "sha256:a84f3f07544634a130d7dc615ae44175419f4c77957a7255161ed99c69c7c8b0"},
 ]
 
 [package.dependencies]
@@ -5050,31 +5123,10 @@ complete = ["xarray[accel,etc,io,parallel,viz]"]
 etc = ["sparse"]
 io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap ; python_version < \"3.10\"", "scipy", "zarr"]
 parallel = ["dask[complete]"]
-types = ["pandas-stubs", "types-PyYAML", "types-Pygments", "types-colorama", "types-decorator", "types-defusedxml", "types-docutils", "types-networkx", "types-openpyxl", "types-pexpect", "types-psutil", "types-pycurl", "types-python-dateutil", "types-pytz", "types-setuptools"]
+types = ["pandas-stubs", "scipy-stubs", "types-PyYAML", "types-Pygments", "types-colorama", "types-decorator", "types-defusedxml", "types-docutils", "types-networkx", "types-openpyxl", "types-pexpect", "types-psutil", "types-pycurl", "types-python-dateutil", "types-pytz", "types-setuptools"]
 viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"]
 
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version == \"3.9\""
-files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.9, <4"
-content-hash = "4ff52719d9c0cfa69bb4d04dfb6cd2542d21f7467c33827aba53cab5048daf35"
+python-versions = ">=3.10, <4"
+content-hash = "e3e3ca4ca8a7e9de638d766e2b84dadce5008b89e88f996c95fdebc8dded6bcc"
diff --git a/pyproject.toml b/pyproject.toml
index 357d951..a7482aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,16 @@
 [project]
 name = "justice"
-version = "0.2.0"
+version = "1.0.0"
 description = "JUSTICE is an open-source Integrated Assessment Modeling Framework for Analysing Policy Implications of Modelling Assumptions"
 authors = [{ name = "PBiswas", email = "p.biswas@tudelft.nl" }]
 license = { text = "BSD-3-Clause" }
 readme = "README.md"
-requires-python = ">=3.9, <4"
+requires-python = ">=3.10, <4"
 dependencies = [
     "fair (==2.1.3)",
     "h5py (==3.13.0)",
-    "platypus-opt (==1.1.0)",
-    "ema-workbench (==2.5.0)",
-    "numpy (>=1.23)",
+    "platypus-opt (==1.4.1)",
+    "ema-workbench (==2.5.3)",
     "scipy (>=1.10.0)",
     "deap (>=1.4.0)",
     "ipyparallel (>=8.6.1)",
diff --git a/run_optimization.py b/run_optimization.py
new file mode 100644
index 0000000..04e3646
--- /dev/null
+++ b/run_optimization.py
@@ -0,0 +1,1151 @@
+"""
+Orchestrates a multi-objective optimization of the JUSTICE model using Borg.
+- Builds the EMA Model (constants, levers, outcomes).
+- Registers the model wrapper with the Borg adapter (direct evaluation).
+- Defines MPI (MS/MM) subclasses that only tweak Borg’s library/settings.
+- Drives EMA’s optimize(...) with convergence metrics (rank 0 only).
+"""
+
+import datetime
+import json
+import os
+import random
+import pandas as pd
+import warnings
+from pathlib import Path
+from typing import Optional
+from justice.util.regional_configuration import build_macro_region_mapping
+from solvers.emodps.rbf import RBF
+import numpy as np
+from ema_workbench import (
+    Model,
+    Policy,
+    RealParameter,
+    ScalarOutcome,
+    CategoricalParameter,
+    ema_logging,
+    MultiprocessingEvaluator,
+    SequentialEvaluator,
+    MPIEvaluator,
+    Constant,
+    Scenario,
+    perform_experiments,
+)
+from ema_workbench.em_framework.optimization import (
+    ArchiveLogger,
+    EpsilonProgress,
+    EpsNSGAII,
+)
+
+from justice.util.EMA_model_wrapper import (
+    model_wrapper_emodps,
+    model_wrapper_momadps,
+    model_wrapper_momadps_single_agent,
+)
+from justice.util.data_loader import DataLoader
+from justice.util.enumerations import (
+    Abatement,
+    DamageFunction,
+    Economy,
+    Evaluator,
+    Optimizer,
+    WelfareFunction,
+)
+from justice.util.model_time import TimeHorizon
+
+from solvers.moea.borg_platypus_adapter import (
+    BorgMOEA,
+    set_ema_context,
+    _ArchiveView,
+    _AlgorithmStub,
+    _create_intermediate_archives,
+)
+from platypus import Solution
+
+SMALL_NUMBER = 1e-9
+warnings.filterwarnings("ignore")
+
+_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+def _mpi_rank() -> int:
+    """Determine MPI/Slurm rank if present, else 0."""
+    for key in ("OMPI_COMM_WORLD_RANK", "PMI_RANK", "SLURM_PROCID", "MPI_RANK"):
+        val = os.environ.get(key)
+        if val is not None:
+            try:
+                return int(val)
+            except ValueError:
+                pass
+    return 0
+
+
+class MSBorgMOEA(BorgMOEA):
+    """Master–Slave Borg (islands = 1)."""
+
+    def __init__(self, problem, epsilons, population_size=None, **kwargs):
+        super().__init__(
+            problem,
+            epsilons,
+            population_size=population_size,
+            # borg_library_path="./libborgms.so",  # NOTE: For mac, use "./libborg.dylib", for linux use "./libborgms.so"
+            borg_library_path=os.path.join(_dir, "solvers", "moea", "libborgms.so"),
+            solve_settings={},
+            seed=None,  # keep Borg's internal RNG
+            direct_evaluation=True,  # use the evaluation function registered in context
+            **kwargs,
+        )
+
+    def run(self, max_evaluations: int):
+        """Run Borg in master–slave MPI mode (islands = 1)."""
+        from solvers.moea.borg import Borg, Configuration
+
+        if self.borg_library_path:
+            Configuration.setBorgLibrary(self.borg_library_path)
+
+        nvars = self.problem.nvars
+        nobjs = self.problem.nobjs
+        nconstr = getattr(self.problem, "nconstr", 0)
+        callback = self._make_callback()
+        borg = Borg(nvars, nobjs, nconstr, callback)
+
+        self._set_bounds(borg)
+        borg.setEpsilons(*self.epsilons)
+
+        if _mpi_rank() == 0:
+            print(f"[MSBorgMOEA] epsilons = {self.epsilons}", flush=True)
+
+        try:
+            Configuration.startMPI()
+            borg_result = borg.solveMPI(
+                islands=1,
+                maxEvaluations=int(max_evaluations),
+                runtime=b"ms.runtime",
+            )
+        finally:
+            Configuration.stopMPI()
+
+        self.result = []
+        if borg_result is not None:
+            for s_borg in borg_result:
+                sol = Solution(self.problem)
+                sol.variables = list(s_borg.getVariables())
+                sol.objectives = list(s_borg.getObjectives())
+                if nconstr:
+                    sol.constraints = list(s_borg.getConstraints())
+                self.result.append(sol)
+            self.nfe = int(max_evaluations)
+        else:
+            self.nfe = 0
+
+        # Update stub
+        self.archive = _ArchiveView(self.result, improvements=len(self.result))
+        self.algorithm = _AlgorithmStub(self.archive)
+
+    def step(self):
+        return
+
+
+class MMBorgMOEA(BorgMOEA):
+    """Multi-Master Borg (requires islands*(workers+1)+1 MPI ranks)."""
+
+    def __init__(self, problem, epsilons, population_size=None, **kwargs):
+        islands = int(os.environ.get("BORG_ISLANDS", "2"))
+        super().__init__(
+            problem,
+            epsilons,
+            population_size=population_size,
+            # borg_library_path="./libborgmm.so",
+            borg_library_path=os.path.join(_dir, "solvers", "moea", "libborgmm.so"),
+            solve_settings={"islands": islands},
+            seed=None,
+            direct_evaluation=True,
+            **kwargs,
+        )
+        self._islands = islands
+
+    def run(self, max_evaluations: int):
+        """Run Borg in multi-master MPI mode."""
+        from solvers.moea.borg import Borg, Configuration
+
+        if self.borg_library_path:
+            Configuration.setBorgLibrary(self.borg_library_path)
+
+        nvars = self.problem.nvars
+        nobjs = self.problem.nobjs
+        nconstr = getattr(self.problem, "nconstr", 0)
+        callback = self._make_callback()
+        borg = Borg(nvars, nobjs, nconstr, callback)
+
+        self._set_bounds(borg)
+        borg.setEpsilons(*self.epsilons)
+
+        if _mpi_rank() == 0:
+            print(
+                f"[MMBorgMOEA] epsilons = {self.epsilons}, islands = {self._islands}",
+                flush=True,
+            )
+
+        runtime_dir = os.environ.get("BORG_RUNTIME_DIR")
+        if runtime_dir is None:
+            raise RuntimeError(
+                "BORG_RUNTIME_DIR is not set. "
+                "Set this environment variable to the desired output folder before running the optimizer."
+            )
+
+        runtime_template = os.path.join(runtime_dir, "mm_%d.runtime").encode("utf-8")
+
+        try:
+            Configuration.startMPI()
+            borg_result = borg.solveMPI(
+                islands=self._islands,
+                maxEvaluations=int(max_evaluations),
+                runtime=runtime_template,
+            )
+        finally:
+            Configuration.stopMPI()
+
+        self.result = []
+        if borg_result is not None:
+            for s_borg in borg_result:
+                sol = Solution(self.problem)
+                sol.variables = list(s_borg.getVariables())
+                sol.objectives = list(s_borg.getObjectives())
+                if nconstr:
+                    sol.constraints = list(s_borg.getConstraints())
+                self.result.append(sol)
+            self.nfe = int(max_evaluations)
+        else:
+            self.nfe = 0
+
+        self.archive = _ArchiveView(self.result, improvements=len(self.result))
+        self.algorithm = _AlgorithmStub(self.archive)
+
+    def step(self):
+        return
+
+
+def run_optimization_adaptive(
+    config_path,
+    nfe=None,
+    population_size=100,
+    swf=0,
+    seed=None,
+    datapath="./data",
+    filename=None,
+    folder=None,
+    economy_type=Economy.NEOCLASSICAL,
+    damage_function_type=DamageFunction.KALKUHL,
+    abatement_type=Abatement.ENERDATA,
+    optimizer=Optimizer.EpsNSGAII,
+    evaluator=Evaluator.SequentialEvaluator,
+    reference_ssp_rcp_scenario_index=2,
+):
+    """Set up the EMA Model, register evaluation context, and run optimize()."""
+    with open(config_path, "r") as file:
+        config = json.load(file)
+
+    start_year = config["start_year"]
+    end_year = config["end_year"]
+    data_timestep = config["data_timestep"]
+    timestep = config["timestep"]
+    emission_control_start_year = config["emission_control_start_year"]
+    n_rbfs = config["n_rbfs"]
+    n_inputs = config["n_inputs"]
+    epsilons = config["epsilons"]
+    temperature_year_of_interest = config["temperature_year_of_interest"]
+    reference_index = reference_ssp_rcp_scenario_index
+    stochastic_run = config["stochastic_run"]
+    climate_members = config.get("climate_ensemble_members")
+
+    social_welfare_function = WelfareFunction.from_index(swf)
+    swf_type = social_welfare_function.value[0]
+
+    model = Model("JUSTICE", function=model_wrapper_emodps)
+
+    data_loader = DataLoader()
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    emission_start_ts = time_horizon.year_to_timestep(
+        year=emission_control_start_year, timestep=timestep
+    )
+    temperature_year_index = time_horizon.year_to_timestep(
+        year=temperature_year_of_interest, timestep=timestep
+    )
+
+    model.constants = [
+        Constant("n_regions", len(data_loader.REGION_LIST)),
+        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
+        Constant("emission_control_start_timestep", emission_start_ts),
+        Constant("n_rbfs", n_rbfs),
+        Constant("n_inputs_rbf", n_inputs),
+        Constant("n_outputs_rbf", len(data_loader.REGION_LIST)),
+        Constant("social_welfare_function_type", swf_type),
+        Constant("economy_type", economy_type.value),
+        Constant("damage_function_type", damage_function_type.value),
+        Constant("abatement_type", abatement_type.value),
+        Constant("temperature_year_of_interest_index", temperature_year_index),
+        Constant("stochastic_run", stochastic_run),
+        Constant("climate_ensemble_members", climate_members),
+    ]
+
+    model.uncertainties = [CategoricalParameter("ssp_rcp_scenario", tuple(range(8)))]
+
+    centers_shape = n_rbfs * n_inputs
+    weights_shape = len(data_loader.REGION_LIST) * n_rbfs
+
+    centers = [RealParameter(f"center {i}", -1.0, 1.0) for i in range(centers_shape)]
+    radii = [
+        RealParameter(f"radii {i}", SMALL_NUMBER, 1.0) for i in range(centers_shape)
+    ]
+    weights = [
+        RealParameter(f"weights {i}", SMALL_NUMBER, 1.0) for i in range(weights_shape)
+    ]
+    model.levers = centers + radii + weights
+
+    model.outcomes = [
+        ScalarOutcome("welfare", variable_name="welfare", kind=ScalarOutcome.MINIMIZE),
+        ScalarOutcome(
+            "fraction_above_threshold",
+            variable_name="fraction_above_threshold",
+            kind=ScalarOutcome.MINIMIZE,
+        ),
+    ]
+
+    reference_scenario = Scenario("reference", ssp_rcp_scenario=reference_index)
+
+    filename = f"{social_welfare_function.value[1]}_{nfe}_{seed}.tar.gz"
+    timestamp = datetime.datetime.now().strftime("%Y_%m")  # _%d_%H_%M_%S
+    random_number = random.randint(0, 10000)
+    directory_name = os.path.abspath(
+        os.path.join(
+            datapath,
+            f"{social_welfare_function.value[1]}_{timestamp}_{random_number}_ref{reference_ssp_rcp_scenario_index}_{seed}",
+        )
+    )
+
+    os.environ["BORG_RUNTIME_DIR"] = directory_name
+    os.makedirs(directory_name, exist_ok=True)
+
+    rank = _mpi_rank()
+    if rank == 0:
+        convergence = [
+            ArchiveLogger(
+                directory_name,
+                [lever.name for lever in model.levers],
+                [outcome.name for outcome in model.outcomes],
+                base_filename=filename,
+            ),
+            EpsilonProgress(),
+        ]
+    else:
+        convergence = []
+
+    if optimizer == Optimizer.EpsNSGAII:
+        algorithm_class = EpsNSGAII
+    elif optimizer == Optimizer.MMBorgMOEA:
+        algorithm_class = MMBorgMOEA
+    elif optimizer == Optimizer.MSBorgMOEA:
+        algorithm_class = MSBorgMOEA
+    else:
+        raise ValueError(f"Unsupported optimizer: {optimizer}")
+
+    set_ema_context(
+        model=model,
+        reference=reference_scenario,
+        evaluation=model_wrapper_emodps,
+        reference_index=reference_index,
+    )
+
+    if evaluator == Evaluator.MPIEvaluator:
+        with MPIEvaluator(model) as _evaluator:
+            results = _evaluator.optimize(
+                searchover="levers",
+                nfe=nfe,
+                epsilons=epsilons,
+                reference=reference_scenario,
+                convergence=convergence,
+                population_size=population_size,
+                algorithm=algorithm_class,
+            )
+    elif evaluator == Evaluator.MultiprocessingEvaluator:
+        with MultiprocessingEvaluator(model) as _evaluator:
+            results = _evaluator.optimize(
+                searchover="levers",
+                nfe=nfe,
+                epsilons=epsilons,
+                reference=reference_scenario,
+                convergence=convergence,
+                population_size=population_size,
+                algorithm=algorithm_class,
+            )
+    else:
+        with SequentialEvaluator(model) as _evaluator:
+            results = _evaluator.optimize(
+                searchover="levers",
+                nfe=nfe,
+                epsilons=epsilons,
+                reference=reference_scenario,
+                convergence=convergence,
+                population_size=population_size,
+                algorithm=algorithm_class,
+            )
+
+    if (
+        rank == 0
+        and optimizer == Optimizer.MMBorgMOEA
+        and os.path.isdir(directory_name)
+    ):
+        header = [lever.name for lever in model.levers] + [
+            outcome.name for outcome in model.outcomes
+        ]
+        islands = int(os.environ.get("BORG_ISLANDS", "2"))
+        _create_intermediate_archives(directory_name, filename, islands, header)
+
+    return results
+
+
+################################################################################################################################
+
+
+def run_optimization_momadps(
+    config_path,
+    nfe=None,
+    population_size=100,
+    seed=None,
+    datapath="./data",
+    economy_type=Economy.NEOCLASSICAL,
+    damage_function_type=DamageFunction.KALKUHL,
+    abatement_type=Abatement.ENERDATA,
+    optimizer=Optimizer.EpsNSGAII,
+    evaluator=Evaluator.SequentialEvaluator,
+    reference_ssp_rcp_scenario_index=2,
+    mapping_base_path="data/input",
+):
+    """Configure and run the MOMADPS optimization experiment."""
+    with open(config_path, "r", encoding="utf-8") as file:
+        config = json.load(file)
+
+    start_year = config["start_year"]
+    end_year = config["end_year"]
+    data_timestep = config["data_timestep"]
+    timestep = config["timestep"]
+    emission_control_start_year = config["emission_control_start_year"]
+    # n_rbfs = config["n_rbfs"]
+    n_inputs = config["n_inputs"]  # expect 3 (T, ΔT, consumption)
+    epsilons = config["epsilons"]
+    temperature_year_of_interest = config["temperature_year_of_interest"]
+    stochastic_run = config["stochastic_run"]
+    climate_members = config.get("climate_ensemble_members")
+
+    min_temperature = config["min_temperature"]
+    max_temperature = config["max_temperature"]
+    min_temperature_change = config["min_temperature_change"]
+    max_temperature_change = config["max_temperature_change"]
+    consumption_min = config["consumption_min"]
+    consumption_max = config["consumption_max"]
+
+    model = Model("JUSTICE", function=model_wrapper_momadps)
+
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+    n_regions = len(region_list)
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    emission_start_ts = time_horizon.year_to_timestep(
+        year=emission_control_start_year, timestep=timestep
+    )
+    temperature_year_index = time_horizon.year_to_timestep(
+        year=temperature_year_of_interest, timestep=timestep
+    )
+
+    # Build macro-region mapping
+    r5_json = Path(mapping_base_path) / "R5_regions.json"
+    rice50_json = Path(mapping_base_path) / "rice50_regions_dict.json"
+    region_to_macro, macro_region_names = build_macro_region_mapping(
+        region_list=region_list,
+        r5_json_path=r5_json,
+        rice50_json_path=rice50_json,
+    )
+    n_macro_regions = len(macro_region_names)
+
+    # Constants fed into the wrapper
+    model.constants = [
+        Constant("n_regions", n_regions),
+        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
+        Constant("emission_control_start_timestep", emission_start_ts),
+        # Constant("n_rbfs", n_rbfs),
+        Constant("n_inputs_rbf", n_inputs),
+        Constant("n_outputs_rbf", 1),
+        Constant(
+            "social_welfare_function_type", WelfareFunction.from_index(0).value[0]
+        ),
+        Constant("economy_type", economy_type.value),
+        Constant("damage_function_type", damage_function_type.value),
+        Constant("abatement_type", abatement_type.value),
+        Constant("temperature_year_of_interest_index", temperature_year_index),
+        Constant("stochastic_run", stochastic_run),
+        Constant("climate_ensemble_members", climate_members),
+        Constant("region_to_macro", region_to_macro.tolist()),
+        Constant("macro_region_names", list(macro_region_names)),
+        Constant("n_macro_regions", n_macro_regions),
+        Constant("min_temperature", min_temperature),
+        Constant("max_temperature", max_temperature),
+        Constant("min_temperature_change", min_temperature_change),
+        Constant("max_temperature_change", max_temperature_change),
+        Constant("consumption_min", consumption_min),
+        Constant("consumption_max", consumption_max),
+    ]
+
+    model.uncertainties = [CategoricalParameter("ssp_rcp_scenario", tuple(range(8)))]
+
+    # Determine lever shapes from an RBF template
+    rbf_probe = RBF(
+        n_rbfs=(n_inputs + 2),
+        n_inputs=n_inputs,
+        n_outputs=1,
+    )
+    centers_shape, radii_shape, weights_shape = rbf_probe.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+
+    levers = []
+    for macro_idx in range(n_macro_regions):
+        levers.extend(
+            RealParameter(f"center {macro_idx} {i}", -1.0, 1.0)
+            for i in range(centers_len)
+        )
+        levers.extend(
+            RealParameter(f"radii {macro_idx} {i}", SMALL_NUMBER, 1.0)
+            for i in range(radii_len)
+        )
+        levers.extend(
+            RealParameter(f"weights {macro_idx} {i}", SMALL_NUMBER, 1.0)
+            for i in range(weights_len)
+        )
+    model.levers = levers
+
+    model.outcomes = [
+        ScalarOutcome(
+            "macro_welfare_R5ASIA",
+            variable_name="macro_welfare_R5ASIA",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5LAM",
+            variable_name="macro_welfare_R5LAM",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5MAF",
+            variable_name="macro_welfare_R5MAF",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5OECD",
+            variable_name="macro_welfare_R5OECD",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5REF",
+            variable_name="macro_welfare_R5REF",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "fraction_above_threshold",
+            variable_name="fraction_above_threshold",
+            kind=ScalarOutcome.MINIMIZE,
+        ),
+    ]
+
+    reference_scenario = Scenario(
+        "reference", ssp_rcp_scenario=reference_ssp_rcp_scenario_index
+    )
+
+    filename = f"MOMADPS_{nfe}_{seed}.tar.gz"
+    timestamp = datetime.datetime.now().strftime("%Y_%m")
+    random_number = random.randint(0, 10000)
+    directory_name = os.path.abspath(
+        os.path.join(
+            datapath,
+            f"MOMADPS_{timestamp}_{random_number}_ref{reference_ssp_rcp_scenario_index}_{seed}",
+        )
+    )
+    os.environ["BORG_RUNTIME_DIR"] = directory_name
+    os.makedirs(directory_name, exist_ok=True)
+
+    rank = _mpi_rank()
+    lever_names = [lever.name for lever in model.levers]
+    outcome_names = [outcome.name for outcome in model.outcomes]
+    if rank == 0:
+        convergence = [
+            ArchiveLogger(
+                directory_name, lever_names, outcome_names, base_filename=filename
+            ),
+            EpsilonProgress(),
+        ]
+    else:
+        convergence = []
+
+    optimizer_map = {
+        Optimizer.EpsNSGAII: EpsNSGAII,
+        Optimizer.MMBorgMOEA: MMBorgMOEA,
+        Optimizer.MSBorgMOEA: MSBorgMOEA,
+    }
+    algorithm_class = optimizer_map.get(optimizer)
+    if algorithm_class is None:
+        raise ValueError(f"Unsupported optimizer: {optimizer}")
+
+    set_ema_context(
+        model=model,
+        reference=reference_scenario,
+        evaluation=model_wrapper_momadps,
+        reference_index=reference_ssp_rcp_scenario_index,
+    )
+
+    evaluator_map = {
+        Evaluator.MPIEvaluator: MPIEvaluator,
+        Evaluator.MultiprocessingEvaluator: MultiprocessingEvaluator,
+        Evaluator.SequentialEvaluator: SequentialEvaluator,
+    }
+    evaluator_cls = evaluator_map[evaluator]
+
+    with evaluator_cls(model) as eval_ctx:
+        results = eval_ctx.optimize(
+            searchover="levers",
+            nfe=nfe,
+            epsilons=epsilons,
+            reference=reference_scenario,
+            convergence=convergence,
+            population_size=population_size,
+            algorithm=algorithm_class,
+        )
+
+    if (
+        rank == 0
+        and optimizer == Optimizer.MMBorgMOEA
+        and os.path.isdir(directory_name)
+    ):
+        header = lever_names + outcome_names
+        islands = int(os.environ.get("BORG_ISLANDS", "2"))
+        _create_intermediate_archives(directory_name, filename, islands, header)
+
+    return results
+
+
+###############################################################################################################################
+def run_single_agent_momadps(
+    config_path: str,
+    nash_profiles_path: str,
+    policy_bank_path: str,
+    policy_index: int,
+    variable_macro_index: int,
+    nfe: Optional[int] = None,
+    population_size: int = 100,
+    seed: Optional[int] = None,
+    datapath: str = "./data",
+    economy_type: Economy = Economy.NEOCLASSICAL,
+    damage_function_type: DamageFunction = DamageFunction.KALKUHL,
+    abatement_type: Abatement = Abatement.ENERDATA,
+    optimizer: Optimizer = Optimizer.EpsNSGAII,
+    evaluator: Evaluator = Evaluator.SequentialEvaluator,
+    reference_ssp_rcp_scenario_index: int = 2,
+    mapping_base_path: str = "data/input",
+    epsilons: Optional[list[float]] = None,
+):
+    """
+    Re-run an adaptive MOMADPS optimization where only one macro agent is allowed to
+    adjust its RBF parameters, while the other four remain fixed at a known Pareto-Nash
+    solution.
+
+    The fixed parameters for each agent are looked up independently:
+      - `nash_profiles_path` (pareto_nash_profiles.csv): row `policy_index` gives
+        per-agent action indices a0..a4.
+      - `policy_bank_path` (COMBINED_MOMA_epsilon_nondominated_set.csv): each agent i
+        reads its RBF parameters from row a_i, which may differ across agents.
+
+    The objectives:
+        * Maximize the selected agent's welfare (macro-specific).
+        * Minimize the fraction of ensemble runs above the temperature threshold.
+    """
+
+    with open(config_path, "r", encoding="utf-8") as file:
+        config = json.load(file)
+
+    # --- Load Nash profile row to get per-agent action indices ---
+    nash_df = pd.read_csv(nash_profiles_path)
+    if policy_index < -len(nash_df) or policy_index >= len(nash_df):
+        raise IndexError(
+            f"policy_index={policy_index} is out of bounds for CSV with {len(nash_df)} rows."
+        )
+    nash_row = nash_df.iloc[int(policy_index)]
+
+    # --- Load the policy bank (5-row CSV) ---
+    policy_bank_df = pd.read_csv(policy_bank_path)
+
+    start_year = config["start_year"]
+    end_year = config["end_year"]
+    data_timestep = config["data_timestep"]
+    timestep = config["timestep"]
+    emission_control_start_year = config["emission_control_start_year"]
+    n_inputs = config["n_inputs"]
+    temperature_year_of_interest = config["temperature_year_of_interest"]
+    stochastic_run = config["stochastic_run"]
+    climate_members = config.get("climate_ensemble_members")
+
+    min_temperature = config["min_temperature"]
+    max_temperature = config["max_temperature"]
+    min_temperature_change = config["min_temperature_change"]
+    max_temperature_change = config["max_temperature_change"]
+    consumption_min = config["consumption_min"]
+    consumption_max = config["consumption_max"]
+
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+    n_regions = len(region_list)
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    emission_start_ts = time_horizon.year_to_timestep(
+        year=emission_control_start_year, timestep=timestep
+    )
+    temperature_year_index = time_horizon.year_to_timestep(
+        year=temperature_year_of_interest, timestep=timestep
+    )
+
+    r5_json = Path(mapping_base_path) / "R5_regions.json"
+    rice50_json = Path(mapping_base_path) / "rice50_regions_dict.json"
+    region_to_macro, macro_region_names = build_macro_region_mapping(
+        region_list=region_list,
+        r5_json_path=r5_json,
+        rice50_json_path=rice50_json,
+    )
+    n_macro_regions = len(macro_region_names)
+
+    if not (0 <= variable_macro_index < n_macro_regions):
+        raise ValueError(
+            f"variable_macro_index={variable_macro_index} invalid; must be in [0, {n_macro_regions-1}]"
+        )
+
+    # --- Extract per-agent action indices from the Nash profile row ---
+    actions = [int(nash_row[f"a{i}"]) for i in range(n_macro_regions)]
+
+    model = Model("JUSTICE", function=model_wrapper_momadps_single_agent)
+
+    rbf_probe = RBF(
+        n_rbfs=(n_inputs + 2),
+        n_inputs=n_inputs,
+        n_outputs=1,
+    )
+    centers_shape, radii_shape, weights_shape = rbf_probe.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+
+    fixed_centers = np.zeros((n_macro_regions, centers_len), dtype=float)
+    fixed_radii = np.zeros((n_macro_regions, radii_len), dtype=float)
+    fixed_weights = np.zeros((n_macro_regions, weights_len), dtype=float)
+
+    # --- Each agent reads from its own row in the policy bank ---
+    for macro_idx in range(n_macro_regions):
+        action_row = policy_bank_df.iloc[actions[macro_idx]]
+        for i in range(centers_len):
+            fixed_centers[macro_idx, i] = action_row[f"center {macro_idx} {i}"]
+            fixed_radii[macro_idx, i] = action_row[f"radii {macro_idx} {i}"]
+        for i in range(weights_len):
+            fixed_weights[macro_idx, i] = action_row[f"weights {macro_idx} {i}"]
+
+    model.constants = [
+        Constant("n_regions", n_regions),
+        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
+        Constant("emission_control_start_timestep", emission_start_ts),
+        Constant("n_inputs_rbf", n_inputs),
+        Constant("n_outputs_rbf", 1),
+        Constant("social_welfare_function_type", WelfareFunction.UTILITARIAN.value[0]),
+        Constant("economy_type", economy_type.value),
+        Constant("damage_function_type", damage_function_type.value),
+        Constant("abatement_type", abatement_type.value),
+        Constant("temperature_year_of_interest_index", temperature_year_index),
+        Constant("stochastic_run", stochastic_run),
+        Constant("climate_ensemble_members", climate_members),
+        Constant("region_to_macro", region_to_macro.tolist()),
+        Constant("macro_region_names", list(macro_region_names)),
+        Constant("n_macro_regions", n_macro_regions),
+        Constant("min_temperature", min_temperature),
+        Constant("max_temperature", max_temperature),
+        Constant("min_temperature_change", min_temperature_change),
+        Constant("max_temperature_change", max_temperature_change),
+        Constant("consumption_min", consumption_min),
+        Constant("consumption_max", consumption_max),
+        Constant("variable_macro_index", variable_macro_index),
+        Constant("fixed_centers", fixed_centers.tolist()),
+        Constant("fixed_radii", fixed_radii.tolist()),
+        Constant("fixed_weights", fixed_weights.tolist()),
+    ]
+
+    algorithm_map = {
+        Optimizer.EpsNSGAII: EpsNSGAII,
+        Optimizer.MMBorgMOEA: MMBorgMOEA,
+        Optimizer.MSBorgMOEA: MSBorgMOEA,
+    }
+    algorithm_class = algorithm_map.get(optimizer)
+    if algorithm_class is None:
+        raise ValueError(f"Unsupported optimizer: {optimizer}")
+
+    model.uncertainties = [CategoricalParameter("ssp_rcp_scenario", tuple(range(8)))]
+
+    levers = []
+    for i in range(centers_len):
+        levers.append(RealParameter(f"center {variable_macro_index} {i}", -1.0, 1.0))
+    for i in range(radii_len):
+        levers.append(
+            RealParameter(f"radii {variable_macro_index} {i}", SMALL_NUMBER, 1.0)
+        )
+    for i in range(weights_len):
+        levers.append(
+            RealParameter(f"weights {variable_macro_index} {i}", SMALL_NUMBER, 1.0)
+        )
+    model.levers = levers
+
+    if epsilons is None:
+        epsilons = [1e-3, 1e-3]
+
+    agent_name = macro_region_names[variable_macro_index]
+    model.outcomes = [
+        ScalarOutcome(f"macro_welfare_{agent_name}", kind=ScalarOutcome.MAXIMIZE),
+        ScalarOutcome("fraction_above_threshold", kind=ScalarOutcome.MINIMIZE),
+    ]
+
+    reference_scenario = Scenario(
+        "reference", ssp_rcp_scenario=reference_ssp_rcp_scenario_index
+    )
+
+    filename = f"SingleAgent_{variable_macro_index}_{nfe}_{seed}.tar.gz"
+    timestamp = datetime.datetime.now().strftime("%Y_%m")
+    random_number = random.randint(0, 10000)
+    directory_name = os.path.abspath(
+        os.path.join(
+            datapath,
+            f"single_agent_{variable_macro_index}_{timestamp}_{random_number}_ref"
+            f"{reference_ssp_rcp_scenario_index}_{seed}",
+        )
+    )
+    os.environ["BORG_RUNTIME_DIR"] = directory_name
+    os.makedirs(directory_name, exist_ok=True)
+
+    rank = _mpi_rank()
+    lever_names = [lever.name for lever in model.levers]
+    outcome_names = [outcome.name for outcome in model.outcomes]
+    if rank == 0:
+        convergence = [
+            ArchiveLogger(
+                directory_name, lever_names, outcome_names, base_filename=filename
+            ),
+            EpsilonProgress(),
+        ]
+    else:
+        convergence = []
+
+    set_ema_context(
+        model=model,
+        reference=reference_scenario,
+        evaluation=model_wrapper_momadps_single_agent,
+        reference_index=reference_ssp_rcp_scenario_index,
+    )
+
+    evaluator_map = {
+        Evaluator.MPIEvaluator: MPIEvaluator,
+        Evaluator.MultiprocessingEvaluator: MultiprocessingEvaluator,
+        Evaluator.SequentialEvaluator: SequentialEvaluator,
+    }
+    evaluator_cls = evaluator_map[evaluator]
+
+    with evaluator_cls(model) as eval_ctx:
+        results = eval_ctx.optimize(
+            searchover="levers",
+            nfe=nfe,
+            epsilons=epsilons,
+            reference=reference_scenario,
+            convergence=convergence,
+            population_size=population_size,
+            algorithm=algorithm_class,
+        )
+
+    if (
+        rank == 0
+        and optimizer == Optimizer.MMBorgMOEA
+        and os.path.isdir(directory_name)
+    ):
+        header = lever_names + outcome_names
+        islands = int(os.environ.get("BORG_ISLANDS", "2"))
+        _create_intermediate_archives(directory_name, filename, islands, header)
+
+    return results
+
+
+###############################################################################################################################
+
+
+def build_random_policy(model, seed=1234):
+    """
+    This is for testing purposes only: builds a random policy within the lever bounds.
+
+    Args:
+        model (ema_workbench.Model): The EMA Workbench model with defined levers.
+        seed (int): Random seed for reproducibility.
+    """
+
+    rng = np.random.default_rng(seed)
+    policy_data = {}
+    for lever in model.levers:
+        low, high = lever.lower_bound, lever.upper_bound
+        policy_data[lever.name] = rng.uniform(low, high)
+    return Policy("random_policy", **policy_data)
+
+
+def setup_model_from_config(config_path, mapping_base_path="data/input"):
+    with open(config_path, "r", encoding="utf-8") as file:
+        config = json.load(file)
+
+    start_year = config["start_year"]
+    end_year = config["end_year"]
+    data_timestep = config["data_timestep"]
+    timestep = config["timestep"]
+    emission_control_start_year = config["emission_control_start_year"]
+    n_rbfs = config["n_rbfs"]
+    n_inputs = config["n_inputs"]
+    temperature_year_of_interest = config["temperature_year_of_interest"]
+    stochastic_run = config["stochastic_run"]
+    climate_members = config.get("climate_ensemble_members")
+
+    min_temperature = config["min_temperature"]
+    max_temperature = config["max_temperature"]
+    min_temperature_change = config["min_temperature_change"]
+    max_temperature_change = config["max_temperature_change"]
+    consumption_min = config["consumption_min"]
+    consumption_max = config["consumption_max"]
+
+    model = Model("JUSTICE", function=model_wrapper_momadps)
+
+    data_loader = DataLoader()
+    region_list = data_loader.REGION_LIST
+    n_regions = len(region_list)
+
+    time_horizon = TimeHorizon(
+        start_year=start_year,
+        end_year=end_year,
+        data_timestep=data_timestep,
+        timestep=timestep,
+    )
+    emission_start_ts = time_horizon.year_to_timestep(
+        year=emission_control_start_year, timestep=timestep
+    )
+    temperature_year_index = time_horizon.year_to_timestep(
+        year=temperature_year_of_interest, timestep=timestep
+    )
+
+    r5_json = Path(mapping_base_path) / "R5_regions.json"
+    rice50_json = Path(mapping_base_path) / "rice50_regions_dict.json"
+    region_to_macro, macro_region_names = build_macro_region_mapping(
+        region_list=region_list,
+        r5_json_path=r5_json,
+        rice50_json_path=rice50_json,
+    )
+    n_macro_regions = len(macro_region_names)
+
+    model.constants = [
+        Constant("n_regions", n_regions),
+        Constant("n_timesteps", len(time_horizon.model_time_horizon)),
+        Constant("emission_control_start_timestep", emission_start_ts),
+        Constant("n_rbfs", n_rbfs),
+        Constant("n_inputs_rbf", n_inputs),
+        Constant("n_outputs_rbf", 1),
+        Constant(
+            "social_welfare_function_type", WelfareFunction.from_index(0).value[0]
+        ),
+        Constant("economy_type", Economy.NEOCLASSICAL.value),
+        Constant("damage_function_type", DamageFunction.KALKUHL.value),
+        Constant("abatement_type", Abatement.ENERDATA.value),
+        Constant("temperature_year_of_interest_index", temperature_year_index),
+        Constant("stochastic_run", stochastic_run),
+        Constant("climate_ensemble_members", climate_members),
+        Constant("region_to_macro", region_to_macro.tolist()),
+        Constant("macro_region_names", list(macro_region_names)),
+        Constant("n_macro_regions", n_macro_regions),
+        Constant("min_temperature", min_temperature),
+        Constant("max_temperature", max_temperature),
+        Constant("min_temperature_change", min_temperature_change),
+        Constant("max_temperature_change", max_temperature_change),
+        Constant("consumption_min", consumption_min),
+        Constant("consumption_max", consumption_max),
+    ]
+
+    model.uncertainties = [CategoricalParameter("ssp_rcp_scenario", tuple(range(8)))]
+
+    rbf_probe = model_wrapper_momadps.__globals__["RBF"](
+        n_rbfs=(n_inputs + 2),
+        n_inputs=n_inputs,
+        n_outputs=1,
+    )
+    centers_shape, radii_shape, weights_shape = rbf_probe.get_shape()
+    centers_len, radii_len, weights_len = (
+        centers_shape[0],
+        radii_shape[0],
+        weights_shape[0],
+    )
+
+    levers = []
+    for macro_idx in range(n_macro_regions):
+        levers.extend(
+            RealParameter(f"center {macro_idx} {i}", -1.0, 1.0)
+            for i in range(centers_len)
+        )
+        levers.extend(
+            RealParameter(f"radii {macro_idx} {i}", SMALL_NUMBER, 1.0)
+            for i in range(radii_len)
+        )
+        levers.extend(
+            RealParameter(f"weights {macro_idx} {i}", SMALL_NUMBER, 1.0)
+            for i in range(weights_len)
+        )
+    model.levers = levers
+
+    model.outcomes = [
+        ScalarOutcome(
+            "macro_welfare_R5ASIA",
+            variable_name="macro_welfare_R5ASIA",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5LAM",
+            variable_name="macro_welfare_R5LAM",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5MAF",
+            variable_name="macro_welfare_R5MAF",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5OECD",
+            variable_name="macro_welfare_R5OECD",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "macro_welfare_R5REF",
+            variable_name="macro_welfare_R5REF",
+            kind=ScalarOutcome.MAXIMIZE,
+        ),
+        ScalarOutcome(
+            "fraction_above_threshold",
+            variable_name="fraction_above_threshold",
+            kind=ScalarOutcome.MINIMIZE,
+        ),
+    ]
+
+    return model, macro_region_names
+
+
+###############################################################################################################################
+
+# EMODPS Runs
+if __name__ == "__main__":
+    config_path = "analysis/normative_uncertainty_optimization.json"
+
+    ema_logging.log_to_stderr(ema_logging.INFO)
+
+    run_optimization_adaptive(
+        config_path=config_path,
+        nfe=10,
+        swf=0,
+        seed=10,  # None for Borg. Any integer for reproducibility with other optimizers
+        datapath="./data",
+        optimizer=Optimizer.MSBorgMOEA,  # Optimizer.MMBorgMOEA, Optimizer.EpsNSGAII
+        population_size=2,  # default is 100. Test locally with 2
+        reference_ssp_rcp_scenario_index=2,  # NOTE #TODO Get this from config json
+        evaluator=Evaluator.SequentialEvaluator,
+    )
+
+
+# Single Agent Runs
+# if __name__ == "__main__":
+# config_path = "analysis/momadps_config.json"
+
+# ema_logging.log_to_stderr(ema_logging.INFO)
+
+# results = run_single_agent_momadps(
+#     config_path="analysis/momadps_config.json",
+#     nash_profiles_path="pareto_nash_profiles.csv",
+#     policy_bank_path="COMBINED_MOMA_epsilon_nondominated_set.csv",
+#     policy_index=9,  # row in pareto_nash_profiles.csv
+#     variable_macro_index=0,  # 0=R5ASIA, 1=R5LAM, etc.
+#     nfe=50,
+#     population_size=2,
+#     epsilons=[1e-3, 1e-3],
+#     seed=10,
+#     datapath="./data",
+#     optimizer=Optimizer.MSBorgMOEA,
+#     reference_ssp_rcp_scenario_index=2,
+# )
+
+# Multi-Agent Runs (full MOMADPS optimization)
+# if __name__ == "__main__":
+#     config_path = "analysis/momadps_config.json"
+
+#     ema_logging.log_to_stderr(ema_logging.INFO)
+
+#     run_optimization_momadps(
+#         config_path=config_path,
+#         nfe=50,
+#         # swf=0,
+#         seed=10,  # None for Borg. Any integer for reproducibility with other optimizers
+#         datapath="./data",
+#         optimizer=Optimizer.MSBorgMOEA,  # Optimizer.MMBorgMOEA, Optimizer.EpsNSGAII
+#         population_size=2,  # default is 100. Test locally with 2
+#         reference_ssp_rcp_scenario_index=2,  # NOTE #TODO Get this from config json
+#         evaluator=Evaluator.SequentialEvaluator,
+#     )
+
+
+###############################################################################################################################
+
+# if __name__ == "__main__":
+#     config_path = "analysis/momadps_config.json"
+
+#     model, macro_region_names = setup_model_from_config(config_path)
+
+#     reference_scenario = Scenario("debug", ssp_rcp_scenario=2)
+#     policy = build_random_policy(model, seed=42)
+
+#     with SequentialEvaluator(model) as evaluator:
+#         experiments, outcomes = perform_experiments(
+#             model,
+#             scenarios=[reference_scenario],
+#             policies=[policy],
+#             evaluator=evaluator,
+#         )
+
+#     print("Experiment outcomes:")
+#     for name, values in outcomes.items():
+#         print(f"{name}: {values}")
+
+#     print("\nPolicy used:")
+#     print(dict(policy))
diff --git a/solvers/moea/borg.py b/solvers/moea/borg.py
new file mode 100644
index 0000000..4a009fe
--- /dev/null
+++ b/solvers/moea/borg.py
@@ -0,0 +1,1077 @@
+"""Python wrapper for the Borg MOEA.
+
+Provides a Python interface for the Borg MOEA.  The Borg MOEA shared library
+(typically named libborg.so or borg.dll) must be located in the same directory
+as this module.  A simple example of using this module is provided below.
+
+    from borg import *
+
+    borg = Borg(2, 2, 0, lambda x,y : [x**2 + y**2, (x-2)**2 + y**2],
+        bounds=[[-50, 50], [-50, 50]],
+        epsilons=[0.01, 0.01])
+
+    for solution in borg.solve({'maxEvaluations':10000}):
+        solution.display()
+
+This wrapper can also run the master-slave and multi-master implementations
+of the Borg MOEA.
+
+    Configuration.startMPI()
+    borg = Borg(...)
+    borg.solveMPI(islands=4, maxTime=1)
+    Configuration.stopMPI()
+
+Please cite the following paper in any works that use or are derived from this
+program.
+
+    Hadka, D. and Reed, P. (2013).  "Borg: An Auto-Adaptive Many-Objective
+    Evolutionary Computing Framework."  Evolutionary Computation,
+    21(2):231-259.
+
+Copyright 2013-2018 David Hadka
+Requires Python 3 or later
+"""
+
+from ctypes import *
+import os
+import sys
+import time
+
+terminate = False
+
+# For Python 3 compatibility.  In Python 3, it appears we must also explicitly set the restype for pointers
+# as c_void_p and also wrap the returned value in c_void_p(...).
+if sys.version_info > (3,):
+    long = int
+
+
+class Configuration:
+    """Holds configuration options for the Borg MOEA Python wrapper."""
+
+    @staticmethod
+    def check():
+        """Checks if the Borg MOEA is initialized and ready to run; otherwise an error is raised."""
+        try:
+            Configuration.libc
+        except:
+            raise OSError(
+                "The standard C library is not defined, please see Configuration.setStandardCLibrary(<file>)"
+            )
+
+        try:
+            Configuration.libborg
+        except:
+            raise OSError(
+                "The Borg MOEA C library is not defined, please see Configuration.setBorgLibrary(<file>)"
+            )
+
+    @staticmethod
+    def initialize():
+        """Initializes the standard C and Borg MOEA libraries."""
+        Configuration.setStandardCLibrary()
+        Configuration.setBorgLibrary()
+        Configuration.seed()
+        Configuration.startedMPI = False
+
+    @staticmethod
+    def setStandardCLibrary(path=None):
+        """Override the standard C library (libc) used by the Python-to-C interface.
+
+        If the path is not specified, this method will attempt to auto-detect the
+        correct location of the standard C library.  If this auto-detection fails,
+        this method will return without error.  This allows the module to load
+        successfully and requires the user to manually invoke this method before
+        using the Borg MOEA.
+        """
+
+        if path:
+            Configuration.libc = CDLL(path)
+        elif sys.platform == "linux":
+            try:
+                Configuration.libc = CDLL("libc.so.6")
+            except OSError:
+                return
+        elif sys.platform == "darwin":
+            try:
+                Configuration.libc = CDLL("libc.dylib")
+            except OSError:
+                return
+        elif sys.platform == "win32" and cdll.msvcrt:
+            Configuration.libc = cdll.msvcrt
+        else:
+            return
+
+        try:
+            Configuration.stdout = Configuration.libc.fdopen(sys.stdout.fileno(), "w")
+        except AttributeError:
+            Configuration.stdout = Configuration.libc._fdopen(sys.stdout.fileno(), "w")
+
+    @staticmethod
+    def setBorgLibrary(path=None):
+        """Override the location of the Borg MOEA shared object.
+
+        If the path is not specified, this method attempts to auto-detect the location
+        of the Borg MOEA C library.  If auto-detection fails, this method returns
+        without error.  This allows the module to load successfully and requires the
+        user to manually invoke this method before using the Borg MOEA
+        """
+
+        if path:
+            try:
+                Configuration.libborg = CDLL(path)
+                Configuration.libborg.BORG_Copyright
+                Configuration.stdcall = False
+            except AttributeError:
+                # Not using __cdecl, try __stdcall instead
+                if sys.platform == "win32":
+                    Configuration.libborg = WinDLL(path)
+                    Configuration.stdcall = True
+        elif sys.platform == "linux":
+            try:
+                _dir = os.path.dirname(os.path.abspath(__file__))
+                Configuration.libborg = CDLL(os.path.join(_dir, "libborg.so"))
+                # Configuration.libborg = CDLL("./libborg.so")
+                Configuration.stdcall = False
+            except OSError:
+                return
+        elif sys.platform == "darwin":
+            try:
+                Configuration.libborg = CDLL("./libborg.dylib")
+                Configuration.stdcall = False
+            except OSError:
+                return
+        elif sys.platform == "win32":
+            try:
+                Configuration.libborg = CDLL("./borg.dll")
+                Configuration.libborg.BORG_Copyright
+                Configuration.stdcall = False
+            except OSError:
+                return
+            except AttributeError:
+                # Not using __cdecl, try __stdcall instead
+                try:
+                    Configuration.libborg = WinDLL("./borg.dll")
+                    Configuration.stdcall = True
+                except OSError:
+                    return
+
+        # Set result type of functions with non-standard types
+        Configuration.libborg.BORG_Problem_create.restype = c_void_p
+        Configuration.libborg.BORG_Operator_create.restype = c_void_p
+        Configuration.libborg.BORG_Algorithm_create.restype = c_void_p
+        Configuration.libborg.BORG_Algorithm_get_result.restype = c_void_p
+        Configuration.libborg.BORG_Archive_get.restype = c_void_p
+        Configuration.libborg.BORG_Solution_get_variable.restype = c_double
+        Configuration.libborg.BORG_Solution_get_objective.restype = c_double
+        Configuration.libborg.BORG_Solution_get_constraint.restype = c_double
+        Configuration.libborg.BORG_Operator_get_probability.restype = c_double
+
+    @staticmethod
+    def seed(value=None):
+        """Sets the pseudo-random number generator seed."""
+        Configuration.check()
+
+        if value:
+            Configuration.libborg.BORG_Random_seed(c_ulong(value))
+        else:
+            Configuration.libborg.BORG_Random_seed(
+                c_ulong(os.getpid() * long(time.time()))
+            )
+
+    @staticmethod
+    def enableDebugging():
+        """Enables debugging output from the Borg MOEA."""
+        Configuration.check()
+        Configuration.libborg.BORG_Debug_on()
+
+    @staticmethod
+    def disableDebugging():
+        """Disables debugging output from the Borg MOEA."""
+        Configuration.check()
+        Configuration.libborg.BORG_Debug_off()
+
+    @staticmethod
+    def displayCopyright():
+        """Displays the copyright message for the Borg MOEA."""
+        Configuration.check()
+        Configuration.libborg.BORG_Copyright(Configuration.stdout)
+
+    @staticmethod
+    def startMPI():
+        """Initializes MPI to enable master-slave and multi-master Borg MOEA runs."""
+        if Configuration.startedMPI:
+            raise RuntimeError("MPI is already started")
+
+        if os.name != "posix":
+            raise RuntimeError("MPI is only supported on Linux")
+        _dir = os.path.dirname(os.path.abspath(__file__))
+        try:
+            Configuration.libborg.BORG_Algorithm_ms_startup
+        except AttributeError:
+            # The serial Borg MOEA C library is loaded; switch to parallel
+            try:
+                # Configuration.setBorgLibrary("./libborgmm.so")
+                Configuration.setBorgLibrary(os.path.join(_dir, "libborgmm.so"))
+            except OSError:
+                try:
+                    Configuration.setBorgLibrary(os.path.join(_dir, "libborgms.so"))
+                    # Configuration.setBorgLibrary("./libborgms.so")
+                except OSError:
+                    raise OSError("Unable to locate the parallel Borg MOEA C library")
+
+        # The following line is needed to load the MPI library correctly
+        CDLL("libmpi.so", RTLD_GLOBAL)
+
+        # Pass the command-line arguments to MPI_Init
+        argc = c_int(len(sys.argv))
+        CHARPP = c_char_p * len(sys.argv)
+        argv = CHARPP()
+
+        for i in range(len(sys.argv)):
+            argv[i] = sys.argv[i].encode("utf-8")
+
+        Configuration.libborg.BORG_Algorithm_ms_startup(
+            cast(addressof(argc), POINTER(c_int)),
+            cast(addressof(argv), POINTER(CHARPP)),
+        )
+
+        Configuration.libborg.BORG_Algorithm_ms_run.restype = c_void_p
+
+        Configuration.startedMPI = True
+
+    @staticmethod
+    def stopMPI():
+        """Shuts down MPI; the master-slave and multi-master Borg MOEA can no longer be used."""
+        if not Configuration.startedMPI:
+            raise RuntimeError("MPI is not started")
+
+        Configuration.libborg.BORG_Algorithm_ms_shutdown()
+        Configuration.startedMPI = False
+
+
+class RestartMode:
+    """Controls the mutation rate during restarts.
+
+    DEFAULT  - The mutation rate is fixed at 1/numberOfVariables
+    RANDOM   - The mutation rate is fixed at 100%
+    RAMPED   - The mutation rates are uniformly sampled between 1/numberOfVariables to 100%
+    ADAPTIVE - The mutation rate adapts based on success of previous restarts
+    INVERTED - Similar to ADAPTIVE, except the rate is inverted
+    """
+
+    DEFAULT = 0
+    RANDOM = 1
+    RAMPED = 2
+    ADAPTIVE = 3
+    INVERTED = 4
+
+
+class ProbabilityMode:
+    """Controls how operator probabilities are adapted.
+
+    DEFAULT  - Operator probabilities based on archive membership
+    RECENCY  - Operator probabilities based on recency (tracks recent additions to archive)
+    BOTH     - Operator probabilities based on archive membership and recency
+    ADAPTIVE - Favors archive membership, but uses recency if insufficient archive size
+    """
+
+    DEFAULT = 0
+    RECENCY = 1
+    BOTH = 2
+    ADAPTIVE = 3
+
+
+class InitializationMode:
+    """Controls how initial populations in the multi-master Borg MOEA are initialized.
+
+    UNIFORM      - Each master starts with a uniformly distributed population
+    LATIN        - Each master starts with a Latin hypercube sampled population
+    GLOBAL_LATIN - A global Latin hypercube sampled population is generated, partitioned,
+               and distributed to the master nodes
+    """
+
+    UNIFORM = 0
+    LATIN = 1
+    GLOBAL_LATIN = 2
+
+
+class Direction:
+    """The optimization direction of an objective (minimized or maximized).
+
+    MINIMIZE - The objective is minimized towards negative infinity
+    MAXIMIZE - The objective is maximized towards positive infinity
+    """
+
+    MINIMIZE = 0
+    MAXIMIZE = 1
+
+
+class Borg:
+    """Solves an optimization problem using the Borg MOEA."""
+
+    def __init__(
+        self,
+        numberOfVariables,
+        numberOfObjectives,
+        numberOfConstraints,
+        function,
+        epsilons=None,
+        bounds=None,
+        directions=None,
+    ):
+        """Creates a new instance of the Borg MOEA.
+
+        numberOfVariables   - The number of decision variables in the optimization problem
+        numberOfObjectives  - The number of objectives in the optimization problem
+        numberOfConstraints - The number of constraints in the optimization problem
+        function            - The function defining the optimization problem
+        epsilons            - The epsilon values for each objective
+        bounds              - The lower and upper bounds for each decision variable
+        directions          - The optimization direction (MINIMIZE or MAXIMIZE) for each objective
+        """
+
+        # Ensure the underlying library is available
+        Configuration.check()
+
+        # Validate input arguments
+        if numberOfVariables < 1:
+            raise ValueError("Requires at least one decision variable")
+
+        if numberOfObjectives < 1:
+            raise ValueError("Requires at least one objective")
+
+        if numberOfConstraints < 0:
+            raise ValueError("Number of constraints can not be negative")
+
+        # Construct Borg object
+        self.numberOfVariables = numberOfVariables
+        self.numberOfObjectives = numberOfObjectives
+        self.numberOfConstraints = numberOfConstraints
+        self.directions = directions
+        self.function = _functionWrapper(
+            function,
+            numberOfVariables,
+            numberOfObjectives,
+            numberOfConstraints,
+            directions,
+        )
+
+        if Configuration.stdcall:
+            self.CMPFUNC = WINFUNCTYPE(
+                c_void_p, POINTER(c_double), POINTER(c_double), POINTER(c_double)
+            )
+        else:
+            self.CMPFUNC = CFUNCTYPE(
+                c_void_p, POINTER(c_double), POINTER(c_double), POINTER(c_double)
+            )
+
+        self.callback = self.CMPFUNC(self.function)
+        self.reference = c_void_p(
+            Configuration.libborg.BORG_Problem_create(
+                c_int(numberOfVariables),
+                c_int(numberOfObjectives),
+                c_int(numberOfConstraints),
+                self.callback,
+            )
+        )
+
+        if bounds:
+            self.setBounds(*bounds)
+        else:
+            self.setBounds(*[[0, 1]] * numberOfVariables)
+
+        if epsilons:
+            self.setEpsilons(*epsilons)
+        else:
+            self.epsilonsAssigned = False
+
+    def __del__(self):
+        """Deletes the underlying C objects."""
+        try:
+            Configuration.libborg.BORG_Problem_destroy(self.reference)
+        except AttributeError:
+            pass
+
+    def setBounds(self, *args):
+        """Sets the decision variable lower and upper bounds.
+
+        The arguments to this function must be 2-ary lists defining the
+        lower and upper bounds.  The number of lists must equal the
+        number of decision variables.  For example:
+            setBounds([0, 1], [-10, 10], [-1, 1])
+        If each decision variable has the same bounds, this can be
+        written compactly:
+            setBounds(*[[0, 1]]*3)
+        """
+
+        if len(args) != self.numberOfVariables:
+            raise ValueError("Incorrect number of bounds specified")
+
+        for i in range(self.numberOfVariables):
+            self._setBounds(i, args[i][0], args[i][1])
+
+    def setEpsilons(self, *args):
+        """Sets the epsilons for the objective values.
+
+        The epsilons control the granularity / resolution of the Pareto
+        optimal set.  Small epsilons typically result in larger Pareto
+        optimal sets, but can reduce runtime performance.  Specify one
+        argument for each objective.  For example:
+            setEpsilons(0.01, 0.5)
+        If all epsilons are the same, this can be written more compactly:
+            setEpsilons(*[0.01]*2)
+        """
+
+        if len(args) != self.numberOfObjectives:
+            raise ValueError("Incorrect number of epsilons specified")
+
+        for i in range(self.numberOfObjectives):
+            self._setEpsilon(i, args[i])
+
+        self.epsilonsAssigned = True
+
+    def _setEpsilon(self, index, value):
+        """Sets the epsilon value at the given index."""
+        Configuration.libborg.BORG_Problem_set_epsilon(
+            self.reference, index, c_double(value)
+        )
+
+    def _setBounds(self, index, lowerBound, upperBound):
+        """Sets the lower and upper decision variable bounds at the given index."""
+        Configuration.libborg.BORG_Problem_set_bounds(
+            self.reference, index, c_double(lowerBound), c_double(upperBound)
+        )
+
+    def solveMPI(
+        self,
+        islands=1,
+        maxTime=None,
+        maxEvaluations=None,
+        initialization=None,
+        runtime=None,
+        allEvaluations=None,
+    ):
+        """Runs the master-slave or multi-master Borg MOEA using MPI.
+
+        islands        - The number of islands
+        maxTime        - The maximum wallclock time to run, in hours
+        maxEvaluations - The maximum NFE per island (total NFE is islands*maxEvaluations)
+        initialization - Controls how the initial populations are generated
+        runtime        - Filename pattern for saving runtime dynamics (the filename should include
+                 one %d which gets replaced by the island index)
+        allEvaluations - Filename pattern for saving all evaluations (the filename should include
+                         one %d which gets replaced by the island index).  Since this can quickly
+                         generate large files, use this option with caution.
+
+        Note: All nodes must invoke solveMPI.  However, only one node will return the discovered
+        Pareto optimal solutions.  The rest will return None.
+        """
+
+        if not self.epsilonsAssigned:
+            raise RuntimeError("Epsilons must be assigned")
+
+        if not Configuration.startedMPI:
+            raise RuntimeError(
+                "MPI is not started; call Configuration.startMPI() first"
+            )
+
+        if not maxTime and not maxEvaluations:
+            raise ValueError("Must specify maxEvaluations or maxTime (or both)")
+
+        if islands > 1:
+            try:
+                Configuration.libborg.BORG_Algorithm_ms_islands(c_int(islands))
+            except AttributeError:
+                raise RuntimeError(
+                    "The loaded Borg MOEA C library does not support multi-master"
+                )
+
+        if maxTime:
+            Configuration.libborg.BORG_Algorithm_ms_max_time(c_double(maxTime))
+
+        if maxEvaluations:
+            Configuration.libborg.BORG_Algorithm_ms_max_evaluations(
+                c_int(maxEvaluations)
+            )
+
+        if initialization and islands > 1:
+            Configuration.libborg.BORG_Algorithm_ms_initialization(
+                c_int(initialization)
+            )
+
+        if runtime:
+            Configuration.libborg.BORG_Algorithm_output_runtime(c_char_p(runtime))
+
+        if allEvaluations:
+            Configuration.libborg.BORG_Algorithm_output_evaluations(
+                c_char_p(allEvaluations)
+            )
+
+        result = c_void_p(Configuration.libborg.BORG_Algorithm_ms_run(self.reference))
+
+        return Result(result, self) if result.value else None
+
+    def solve(self, settings={}):
+        """Runs the Borg MOEA to solve the defined optimization problem, returning the
+        discovered Pareto optimal set.
+
+        settings - Dictionary of parameters for the Borg MOEA.  The key should match one
+               of the parameters defined by the C Borg API.  Default parameter values
+               are used for any undefined parameters.
+        """
+
+        if not self.epsilonsAssigned:
+            raise RuntimeError("Epsilons must be set")
+
+        maxEvaluations = settings.get("maxEvaluations", 10000)
+        start = time.process_time()
+
+        pm = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "PM", 1, 1, 2, Configuration.libborg.BORG_Operator_PM
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            pm, 0, c_double(settings.get("pm.rate", 1.0 / self.numberOfVariables))
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            pm, 1, c_double(settings.get("pm.distributionIndex", 20.0))
+        )
+
+        sbx = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "SBX", 2, 2, 2, Configuration.libborg.BORG_Operator_SBX
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            sbx, 0, c_double(settings.get("sbx.rate", 1.0))
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            sbx, 1, c_double(settings.get("sbx.distributionIndex", 15.0))
+        )
+        Configuration.libborg.BORG_Operator_set_mutation(sbx, pm)
+
+        de = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "DE", 4, 1, 2, Configuration.libborg.BORG_Operator_DE
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            de, 0, c_double(settings.get("de.crossoverRate", 0.1))
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            de, 1, c_double(settings.get("de.stepSize", 0.5))
+        )
+        Configuration.libborg.BORG_Operator_set_mutation(de, pm)
+
+        um = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "UM", 1, 1, 1, Configuration.libborg.BORG_Operator_UM
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            um, 0, c_double(settings.get("um.rate", 1.0 / self.numberOfVariables))
+        )
+
+        spx = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "SPX",
+                c_int(settings.get("spx.parents", 10)),
+                c_int(settings.get("spx.offspring", 2)),
+                1,
+                Configuration.libborg.BORG_Operator_SPX,
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            spx, 0, c_double(settings.get("spx.epsilon", 3.0))
+        )
+
+        pcx = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "PCX",
+                c_int(settings.get("pcx.parents", 10)),
+                c_int(settings.get("pcx.offspring", 2)),
+                2,
+                Configuration.libborg.BORG_Operator_PCX,
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            pcx, 0, c_double(settings.get("pcx.eta", 0.1))
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            pcx, 1, c_double(settings.get("pcx.zeta", 0.1))
+        )
+
+        undx = c_void_p(
+            Configuration.libborg.BORG_Operator_create(
+                "UNDX",
+                c_int(settings.get("undx.parents", 10)),
+                c_int(settings.get("undx.offspring", 2)),
+                2,
+                Configuration.libborg.BORG_Operator_UNDX,
+            )
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            undx, 0, c_double(settings.get("undx.zeta", 0.5))
+        )
+        Configuration.libborg.BORG_Operator_set_parameter(
+            undx, 1, c_double(settings.get("undx.eta", 0.35))
+        )
+
+        algorithm = c_void_p(
+            Configuration.libborg.BORG_Algorithm_create(self.reference, 6)
+        )
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 0, sbx)
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 1, de)
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 2, pcx)
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 3, spx)
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 4, undx)
+        Configuration.libborg.BORG_Algorithm_set_operator(algorithm, 5, um)
+
+        Configuration.libborg.BORG_Algorithm_set_initial_population_size(
+            algorithm, c_int(settings.get("initialPopulationSize", 100))
+        )
+        Configuration.libborg.BORG_Algorithm_set_minimum_population_size(
+            algorithm, c_int(settings.get("minimumPopulationSize", 100))
+        )
+        Configuration.libborg.BORG_Algorithm_set_maximum_population_size(
+            algorithm, c_int(settings.get("maximumPopulationSize", 10000))
+        )
+        Configuration.libborg.BORG_Algorithm_set_population_ratio(
+            algorithm, c_double(1.0 / settings.get("injectionRate", 0.25))
+        )
+        Configuration.libborg.BORG_Algorithm_set_selection_ratio(
+            algorithm, c_double(settings.get("selectionRatio", 0.02))
+        )
+        Configuration.libborg.BORG_Algorithm_set_restart_mode(
+            algorithm, c_int(settings.get("restartMode", RestartMode.DEFAULT))
+        )
+        Configuration.libborg.BORG_Algorithm_set_max_mutation_index(
+            algorithm, c_int(settings.get("maxMutationIndex", 10))
+        )
+        Configuration.libborg.BORG_Algorithm_set_probability_mode(
+            algorithm, c_int(settings.get("probabilityMode", ProbabilityMode.DEFAULT))
+        )
+
+        runtimeformat = settings.get("runtimeformat", "optimizedv")
+        fp = None
+        if "frequency" in settings:
+            statistics = []
+            lastSnapshot = 0
+            frequency = settings.get("frequency")
+            if "runtimefile" in settings:
+                fp = open(settings["runtimefile"], "w")
+                if runtimeformat == "optimizedv":
+                    fp.write("//")
+                    dynamics_header = [
+                        "NFE",
+                        "ElapsedTime",
+                        "SBX",
+                        "DE",
+                        "PCX",
+                        "SPX",
+                        "UNDX",
+                        "UM",
+                        "Improvements",
+                        "Restarts",
+                        "PopulationSize",
+                        "ArchiveSize",
+                    ]
+                    if settings.get("restartMode", None) == RestartMode.ADAPTIVE:
+                        dynamics_header.append("MutationIndex")
+                    fp.write(",".join(dynamics_header))
+                    fp.write("\n")
+                    header = (
+                        ["NFE"]
+                        + ["dv{0}".format(i) for i in range(self.numberOfVariables)]
+                        + ["obj{0}".format(i) for i in range(self.numberOfObjectives)]
+                        + ["con{0}".format(i) for i in range(self.numberOfConstraints)]
+                    )
+                    fp.write(",".join(header))
+                    fp.write("\n")
+                    fp.flush()
+            else:
+                fp = None
+        else:
+            statistics = None
+
+        data_header_written = False
+        while Configuration.libborg.BORG_Algorithm_get_nfe(algorithm) < maxEvaluations:
+            Configuration.libborg.BORG_Algorithm_step(algorithm)
+            if terminate is True:
+                break
+            currentEvaluations = Configuration.libborg.BORG_Algorithm_get_nfe(algorithm)
+
+            if (
+                statistics is not None
+                and currentEvaluations - lastSnapshot >= frequency
+            ):
+                entry = {}
+                entry["NFE"] = currentEvaluations
+                entry["ElapsedTime"] = time.process_time() - start
+                entry["SBX"] = Configuration.libborg.BORG_Operator_get_probability(sbx)
+                entry["DE"] = Configuration.libborg.BORG_Operator_get_probability(de)
+                entry["PCX"] = Configuration.libborg.BORG_Operator_get_probability(pcx)
+                entry["SPX"] = Configuration.libborg.BORG_Operator_get_probability(spx)
+                entry["UNDX"] = Configuration.libborg.BORG_Operator_get_probability(
+                    undx
+                )
+                entry["UM"] = Configuration.libborg.BORG_Operator_get_probability(um)
+                entry["Improvements"] = (
+                    Configuration.libborg.BORG_Algorithm_get_number_improvements(
+                        algorithm
+                    )
+                )
+                entry["Restarts"] = (
+                    Configuration.libborg.BORG_Algorithm_get_number_restarts(algorithm)
+                )
+                entry["PopulationSize"] = (
+                    Configuration.libborg.BORG_Algorithm_get_population_size(algorithm)
+                )
+                entry["ArchiveSize"] = (
+                    Configuration.libborg.BORG_Algorithm_get_archive_size(algorithm)
+                )
+
+                if (
+                    settings.get("restartMode", RestartMode.DEFAULT)
+                    == RestartMode.ADAPTIVE
+                ):
+                    entry["MutationIndex"] = (
+                        Configuration.libborg.BORG_Algorithm_get_mutation_index(
+                            algorithm
+                        )
+                    )
+                if fp is None:
+                    statistics.append(entry)
+                else:
+                    archive = Result(
+                        c_void_p(
+                            Configuration.libborg.BORG_Algorithm_get_result(algorithm)
+                        ),
+                        self,
+                        statistics,
+                    )
+                    if runtimeformat == "optimizedv":
+                        row = [
+                            "{0}".format(entry[dynamic]) for dynamic in dynamics_header
+                        ]
+                        fp.write("//")
+                        fp.write(",".join(row))
+                        fp.write("\n")
+                        delimiter = ","
+                    elif runtimeformat == "borg":
+                        metrics = [
+                            ("NFE", "d"),
+                            ("ElapsedTime", ".17g"),
+                            ("SBX", ".17g"),
+                            ("DE", ".17g"),
+                            ("PCX", ".17g"),
+                            ("SPX", ".17g"),
+                            ("UNDX", ".17g"),
+                            ("UM", ".17g"),
+                            ("Improvements", "d"),
+                            ("Restarts", "d"),
+                            ("PopulationSize", "d"),
+                            ("ArchiveSize", "d"),
+                        ]
+                        for metric, fmt in metrics:
+                            fp.write(
+                                "//{0}={1}\n".format(
+                                    metric, "".join(["{0:", fmt, "}"])
+                                ).format(entry[metric])
+                            )
+                        if "MutationIndex" in entry:
+                            fp.write(
+                                "//MutationIndex={0:d}\n".format(entry["MutationIndex"])
+                            )
+                        if "data_header" in settings and data_header_written is False:
+                            data_header_written = True
+                            data_header = [
+                                "_".join(x.split(" ")) for x in settings["data_header"]
+                            ]
+                            data_header.insert(0, "NFE")
+                            fp.write(" ".join(data_header))
+                            fp.write("\n")
+                        delimiter = " "
+
+                    for solution in archive:
+                        report = [entry["NFE"]]
+                        report.extend(solution.getVariables())
+                        report.extend(solution.getObjectives())
+                        report.extend(solution.getConstraints())
+                        fp.write(delimiter.join("{0}".format(v) for v in report))
+                        fp.write("\n")
+                    fp.flush()
+
+                lastSnapshot = currentEvaluations
+
+        result = c_void_p(Configuration.libborg.BORG_Algorithm_get_result(algorithm))
+
+        if fp is not None:
+            fp.close()
+
+        Configuration.libborg.BORG_Operator_destroy(sbx)
+        Configuration.libborg.BORG_Operator_destroy(de)
+        Configuration.libborg.BORG_Operator_destroy(pm)
+        Configuration.libborg.BORG_Operator_destroy(um)
+        Configuration.libborg.BORG_Operator_destroy(spx)
+        Configuration.libborg.BORG_Operator_destroy(pcx)
+        Configuration.libborg.BORG_Operator_destroy(undx)
+        Configuration.libborg.BORG_Algorithm_destroy(algorithm)
+
+        return Result(result, self, statistics)
+
+
+class Solution:
+    """A solution to the optimization problem."""
+
+    def __init__(self, reference, problem):
+        """Creates a solution given a reference to the underlying C object."""
+        self.reference = reference
+        self.problem = problem
+
+    # There is no __del__ since the underlying C solutions are deleted when the associated
+    # result object is deleted
+
+    def getVariables(self):
+        """Returns the decision variable values for this solution."""
+        return [self._getVariable(i) for i in range(self.problem.numberOfVariables)]
+
+    def getObjectives(self):
+        """Returns the objective values for this solution."""
+        return [self._getObjective(i) for i in range(self.problem.numberOfObjectives)]
+
+    def getConstraints(self):
+        """Returns the constraint values for this solution."""
+        return [self._getConstraint(i) for i in range(self.problem.numberOfConstraints)]
+
+    def _getVariable(self, index):
+        """Returns the decision variable at the given index."""
+        return Configuration.libborg.BORG_Solution_get_variable(self.reference, index)
+
+    def _getObjective(self, index):
+        """Returns the objective value at the given index."""
+        value = Configuration.libborg.BORG_Solution_get_objective(self.reference, index)
+
+        if self.problem.directions and self.problem.directions[index]:
+            return -value
+        else:
+            return value
+
+    def _getConstraint(self, index):
+        """Returns the constraint value at the given index."""
+        return Configuration.libborg.BORG_Solution_get_constraint(self.reference, index)
+
+    def display(self, out=sys.stdout, separator=" "):
+        """Prints the decision variables, objectives, and constraints to standard output."""
+        print(
+            separator.join(
+                map(
+                    str,
+                    self.getVariables() + self.getObjectives() + self.getConstraints(),
+                )
+            ),
+            file=out,
+        )
+
+    def violatesConstraints(self):
+        """Returns True if this solution violates one or more constraints; False otherwise."""
+        return (
+            Configuration.libborg.BORG_Solution_violates_constraints(self.reference)
+            != 0
+        )
+
+
+class Result:
+    """A Pareto optimal set (the output of the Borg MOEA)."""
+
+    def __init__(self, reference, problem, statistics=None):
+        """Creates a new Pareto optimal set given a reference to the underlying C object."""
+        self.reference = reference
+        self.problem = problem
+        self.statistics = statistics
+
+    def __del__(self):
+        """Deletes the underlying C objects."""
+        Configuration.libborg.BORG_Archive_destroy(self.reference)
+
+    def __iter__(self):
+        """Returns an iterator over the Pareto optimal solutions."""
+        return ResultIterator(self)
+
+    def display(self, out=sys.stdout, separator=" "):
+        """Print the Pareto optimal solutions to standard output."""
+        for solution in self:
+            solution.display(out, separator)
+
+    def size(self):
+        """Returns the size of the Pareto optimal set."""
+        return Configuration.libborg.BORG_Archive_get_size(self.reference)
+
+    def get(self, index):
+        """Returns the Pareto optimal solution at the given index."""
+        return Solution(
+            c_void_p(Configuration.libborg.BORG_Archive_get(self.reference, index)),
+            self.problem,
+        )
+
+
+class ResultIterator:
+    """Iterates over the solutions in a Pareto optimal set."""
+
+    def __init__(self, result):
+        """Creates an iterator over the given Pareto optimal set."""
+        self.result = result
+        self.index = -1
+
+    def next(self):
+        """Returns the next Pareto optimal solution in the set."""
+        self.index = self.index + 1
+
+        if self.index >= self.result.size():
+            raise StopIteration
+        else:
+            return self.result.get(self.index)
+
+    __next__ = next
+
+
+def _functionWrapper(
+    function,
+    numberOfVariables,
+    numberOfObjectives,
+    numberOfConstraints,
+    directions=None,
+):
+    """Wraps a Python evaluation function and converts it to the function signature
+    required by the C API.
+
+    function - The Python evaluation function of the form (o, c) = f(v)
+    numberOfVariables - The number of decision variables
+    numberOfObjectives - The number of objectives
+    numberOfConstraints - The number of constraints
+    directions - The array of optimization directions
+    """
+
+    def innerFunction(v, o, c):
+        """The function that gets passed to the C API.
+
+        v - The array of decision variables (input)
+        o - The array of objectives (output)
+        c - The array of constraint values (output)
+        """
+        global terminate
+        try:
+            result = function(*[v[i] for i in range(numberOfVariables)])
+            objectives = None
+            constraints = None
+
+            if isinstance(result, tuple):
+                if len(result) > 0:
+                    objectives = result[0]
+                if len(result) > 1:
+                    constraints = result[1]
+            elif isinstance(result, list):
+                objectives = result
+            else:
+                objectives = [result]
+
+            if objectives:
+                if len(objectives) != numberOfObjectives:
+                    raise ValueError(
+                        "Incorrect number of objectives returned by function"
+                    )
+                for i in range(len(objectives)):
+                    if directions and directions[i]:
+                        o[i] = -objectives[i]
+                    else:
+                        o[i] = objectives[i]
+            elif numberOfObjectives > 0:
+                raise ValueError("No objectives returned by function")
+
+            if constraints:
+                if len(constraints) != numberOfConstraints:
+                    raise ValueError(
+                        "Incorrect number of constraints returned by function"
+                    )
+                for i in range(len(constraints)):
+                    c[i] = constraints[i]
+            elif numberOfConstraints > 0:
+                raise ValueError("No constraints returned by function")
+
+            return 0
+        except KeyboardInterrupt:
+            terminate = True
+            return 1
+
+    return innerFunction
+
+
+class Constraint:
+    """Helper functions for defining constraints.
+
+    These functions ensure several conditions hold.  First, if the
+    constraint is satisfied, the value is 0.  If the constraint is
+    violated, then the value is non-zero and will scale linearly
+    with the degree of violation.
+    """
+
+    precision = 0.1
+
+    @staticmethod
+    def greaterThan(x, y, epsilon=0.0):
+        """Defines the constraint x > y."""
+        return 0.0 if x > y - epsilon else y - x + Constraint.precision
+
+    @staticmethod
+    def lessThan(x, y, epsilon=0.0):
+        """Defines the constraint x < y."""
+        return 0.0 if x < y + epsilon else x - y + Constraint.precision
+
+    @staticmethod
+    def greaterThanOrEqual(x, y, epsilon=0.0):
+        """Defines the constraint x >= y."""
+        return 0.0 if x >= y - epsilon else y - x + Constraint.precision
+
+    @staticmethod
+    def lessThanOrEqual(x, y, epsilon=0.0):
+        """Defines the constraint x <= y."""
+        return 0.0 if x <= y + epsilon else x - y + Constraint.precision
+
+    @staticmethod
+    def equal(x, y, epsilon=0.0):
+        """Defines the constraint x == y."""
+        return 0.0 if abs(y - x) < epsilon else abs(y - x) + Constraint.precision
+
+    @staticmethod
+    def zero(x, epsilon=0.0):
+        """Defines the constraint x == 0."""
+        return Constraint.equal(x, 0.0, epsilon)
+
+    @staticmethod
+    def nonNegative(x, epsilon=0.0):
+        """Defines the constraint x >= 0."""
+        return Constraint.greaterThanOrEqual(x, 0.0, epsilon)
+
+    @staticmethod
+    def positive(x, epsilon=0.0):
+        """Defines the constraint x > 0."""
+        return Constraint.greaterThan(x, 0.0, epsilon)
+
+    @staticmethod
+    def negative(x, epsilon=0.0):
+        """Defines the constraint x < 0."""
+        return Constraint.lessThan(x, 0.0, epsilon)
+
+    @staticmethod
+    def all(*args):
+        """Requires all conditions to be satisfied."""
+        return sum(args)
+
+    @staticmethod
+    def any(*args):
+        """Requres at least one condition to be satisfied."""
+        return 0.0 if 0.0 in args else sum(args)
+
+
+Configuration.initialize()
diff --git a/solvers/moea/borg_platypus_adapter.py b/solvers/moea/borg_platypus_adapter.py
new file mode 100644
index 0000000..4a6e277
--- /dev/null
+++ b/solvers/moea/borg_platypus_adapter.py
@@ -0,0 +1,496 @@
+"""
+Adapter that wraps the C Borg implementation (via borg.py) so it can be used as
+a Platypus Algorithm inside EMA-Workbench.
+
+Highlights
+----------
+- Stores EMA model/Scenario/lever names in a shared context.
+- Builds a callback that either evaluates solutions via Platypus’ Problem
+  (default) or directly via a user-specified evaluation function (direct mode).
+- Presents minimal archive stubs so EMA convergence metrics keep working.
+"""
+
+from typing import List, Optional, Dict, Any, Iterable
+import os
+import tarfile
+
+import csv
+import zipfile
+import numpy as np
+from platypus import Algorithm, Problem, Solution, Real, Integer, Binary
+
+# Shared EMA context; set once from user code
+_EMA_CONTEXT: Dict[str, Any] = {
+    "model": None,
+    "reference": None,
+    "lever_names": None,
+    "outcome_names": None,
+    "evaluation": None,
+    "reference_index": None,
+}
+
+
+def _write_block_csv(header, rows, target_dir, nfe):
+    if not rows:
+        return
+    os.makedirs(target_dir, exist_ok=True)
+    csv_path = os.path.join(target_dir, f"{nfe}.csv")
+    with open(csv_path, "w", newline="") as csv_file:
+        writer = csv.writer(csv_file)
+        writer.writerow(header)
+        writer.writerows(rows)
+
+
+def _process_runtime_file(runtime_path, header, island_dir):
+    if not os.path.exists(runtime_path):
+        return
+
+    current_nfe = None
+    rows = []
+
+    with open(runtime_path, "r") as fh:
+        for raw_line in fh:
+            line = raw_line.strip()
+            if not line:
+                continue
+
+            if line == "#":
+                if current_nfe is not None and rows:
+                    _write_block_csv(header, rows, island_dir, current_nfe)
+                current_nfe = None
+                rows = []
+                continue
+
+            if line.startswith("//NFE="):
+                try:
+                    nfe_val = int(line.split("=", 1)[1])
+                except ValueError:
+                    nfe_val = None
+                if current_nfe is not None and rows:
+                    _write_block_csv(header, rows, island_dir, current_nfe)
+                current_nfe = nfe_val
+                rows = []
+                continue
+
+            if line.startswith("//"):
+                continue
+
+            if current_nfe is None:
+                continue
+
+            rows.append(line.split())
+
+    if current_nfe is not None and rows:
+        _write_block_csv(header, rows, island_dir, current_nfe)
+
+
+def _find_final_tar(directory_name, explicit_filename=None):
+    if explicit_filename:
+        candidate = os.path.join(directory_name, explicit_filename)
+        if os.path.isfile(candidate):
+            return candidate
+    for entry in os.listdir(directory_name):
+        if entry.endswith(".tar") or entry.endswith(".tar.gz"):
+            candidate = os.path.join(directory_name, entry)
+            if os.path.isfile(candidate):
+                return candidate
+    return None
+
+
+def _extract_latest_csv_from_tar(tar_path):
+    try:
+        with tarfile.open(tar_path, "r:*") as tar:
+            members = [
+                m for m in tar.getmembers() if m.isfile() and m.name.endswith(".csv")
+            ]
+            if not members:
+                return None
+
+            def _key(member):
+                base = os.path.basename(member.name)
+                digits = "".join(ch for ch in base if ch.isdigit())
+                try:
+                    return int(digits)
+                except ValueError:
+                    return -1
+
+            chosen = max(members, key=_key)
+            extracted = tar.extractfile(chosen)
+            if extracted:
+                return os.path.basename(chosen.name), extracted.read()
+    except (tarfile.TarError, OSError):
+        pass
+    return None
+
+
+def _create_intermediate_archives(directory_name, filename, islands, header):
+    if header is None:
+        return
+    intermediate_root = os.path.join(directory_name, "mm_intermediate")
+    for idx in range(islands):
+        runtime_path = os.path.join(directory_name, f"mm_{idx}.runtime")
+        island_dir = os.path.join(intermediate_root, f"mm_{idx}")
+        _process_runtime_file(runtime_path, header, island_dir)
+    if not os.path.isdir(intermediate_root):
+        return
+    zip_path = os.path.join(directory_name, "mm_intermediate.zip")
+    with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
+        for root, _, files in os.walk(intermediate_root):
+            for fname in files:
+                full_path = os.path.join(root, fname)
+                arcname = os.path.relpath(full_path, directory_name)
+                zf.write(full_path, arcname)
+        tar_path = _find_final_tar(directory_name, filename)
+        if tar_path:
+            zf.write(tar_path, os.path.basename(tar_path))
+            extracted = _extract_latest_csv_from_tar(tar_path)
+            if extracted:
+                csv_name, data = extracted
+                zf.writestr(os.path.join("final_archive", csv_name), data)
+
+
+def set_ema_context(
+    model=None,
+    reference=None,
+    lever_names=None,
+    outcome_names=None,
+    evaluation=None,
+    reference_index=None,
+) -> None:
+    """
+    Populate the EMA context so the Borg callback can fetch constants, levers,
+    outcomes, evaluation function, and reference scenario.
+
+    Parameters
+    ----------
+    model : ema_workbench.Model
+        Model object (used for constants/levers/outcomes).
+    reference : ema_workbench.Scenario
+        Scenario used as the reference case.
+    lever_names : list[str], optional
+        Lever names (if not provided, tries to infer from model).
+    outcome_names : list[str], optional
+        Outcome names (if not provided, tries to infer from model).
+    evaluation : callable, optional
+        Function to evaluate a single policy (e.g., model_wrapper_emodps).
+        Signature should be ``evaluation(**kwargs) -> Tuple[float, float]`` or similar.
+    reference_index : int, optional
+        Optional scenario index so the callback can set `ssp_rcp_scenario`.
+    """
+    _EMA_CONTEXT["model"] = model
+    _EMA_CONTEXT["reference"] = reference
+    _EMA_CONTEXT["evaluation"] = evaluation
+    _EMA_CONTEXT["reference_index"] = reference_index
+
+    if model is not None:
+        try:
+            if lever_names is None and hasattr(model, "levers"):
+                lever_names = [lever.name for lever in model.levers]
+        except Exception:
+            pass
+        try:
+            if outcome_names is None and hasattr(model, "outcomes"):
+                outcome_names = [outcome.name for outcome in model.outcomes]
+        except Exception:
+            pass
+
+    _EMA_CONTEXT["lever_names"] = lever_names
+    _EMA_CONTEXT["outcome_names"] = outcome_names
+
+
+class _ArchiveView:
+    """Minimal archive stub so EMA convergence metrics can iterate/inspect."""
+
+    def __init__(self, solutions: List[Solution], improvements: int = 0) -> None:
+        self._solutions = solutions
+        self.improvements = improvements if improvements is not None else len(solutions)
+
+    def __len__(self) -> int:
+        return len(self._solutions)
+
+    def __iter__(self) -> Iterable[Solution]:
+        return iter(self._solutions)
+
+
+class _AlgorithmStub:
+    """Minimal algorithm stub exposing only an `.archive` attribute."""
+
+    def __init__(self, archive: _ArchiveView) -> None:
+        self.archive = archive
+
+
+class BorgMOEA(Algorithm):
+    """
+    Base adapter for the C Borg MOEA (via borg.py) so it can ‘look like’ a
+    Platypus Algorithm inside EMA-Workbench.
+
+    Parameters
+    ----------
+    problem : platypus.Problem
+        Platypus problem (variables, objectives, constraints).
+    epsilons : List[float]
+        Epsilon values for Borg’s epsilon-dominance archive.
+    population_size : Optional[int]
+        Initial population size (overrides Borg default).
+    borg_library_path : Optional[str]
+        Path to the compiled Borg shared library (e.g., libborg.so or libborgmm.so).
+    seed : Optional[int]
+        Seed for Borg’s RNG (use None to keep Borg’s default randomness).
+    solve_settings : Optional[Dict[str, Any]]
+        Additional Borg run settings (maxEvaluations is set automatically).
+    direct_evaluation : bool
+        When True, the callback calls the evaluation function stored in
+        `_EMA_CONTEXT["evaluation"]` instead of `Problem.evaluate`.
+    """
+
+    def __init__(
+        self,
+        problem: Problem,
+        epsilons: List[float],
+        population_size: Optional[int] = None,
+        borg_library_path: Optional[str] = None,
+        seed: Optional[int] = None,
+        solve_settings: Optional[Dict[str, Any]] = None,
+        direct_evaluation: bool = False,
+        **kwargs,
+    ):
+        super(BorgMOEA, self).__init__(problem)
+
+        # Configuration parameters
+        self.epsilons = epsilons
+        self.population_size = population_size
+        self.borg_library_path = borg_library_path
+        self.seed = seed
+        self.solve_settings = solve_settings or {}
+        self.direct_evaluation = direct_evaluation
+
+        # Extract optional EMA metadata if provided
+        self.evaluator = kwargs.get("evaluator", None)
+        self.reference = kwargs.get("reference", None)
+        self.extra_kwargs = kwargs
+
+        # Try inferring lever/outcome names from evaluator, else fallback to context
+        self._lever_names = None
+        self._outcome_names = None
+        try:
+            if self.evaluator is not None and hasattr(self.evaluator, "model"):
+                self._lever_names = [
+                    lever.name for lever in getattr(self.evaluator.model, "levers", [])
+                ]
+                self._outcome_names = [
+                    outcome.name
+                    for outcome in getattr(self.evaluator.model, "outcomes", [])
+                ]
+        except Exception:
+            pass
+
+        if not self._lever_names:
+            self._lever_names = _EMA_CONTEXT.get("lever_names")
+        if not self._outcome_names:
+            self._outcome_names = _EMA_CONTEXT.get("outcome_names")
+        if self.reference is None:
+            self.reference = _EMA_CONTEXT.get("reference")
+
+        # Initialize result tracking (list of Platypus solutions)
+        self.result: List[Solution] = []
+        self.nfe = 0
+
+        # Provide stub archive/algorithm for EMA convergence logging
+        self.archive = _ArchiveView(self.result, improvements=0)
+        self.algorithm = _AlgorithmStub(self.archive)
+
+    # ------------------------------------------------------------------
+    # Helpers for the two evaluation modes
+    # ------------------------------------------------------------------
+    def _evaluate_with_problem(self, casted_vars: List[Any]) -> tuple:
+        """Evaluate using Problem.evaluate (Platypus default)."""
+        problem = self.problem
+        nconstr = getattr(problem, "nconstr", 0)
+
+        s = Solution(problem)
+        s.variables = casted_vars
+        problem.evaluate(s)
+
+        if nconstr:
+            cons = list(getattr(s, "constraints", []) or [])
+            if len(cons) < nconstr:
+                cons += [0.0] * (nconstr - len(cons))
+            return (list(s.objectives), cons)
+        else:
+            return list(s.objectives)
+
+    def _evaluate_directly(self, casted_vars: List[Any]) -> tuple:
+        eval_fn = _EMA_CONTEXT.get("evaluation")
+        mdl = _EMA_CONTEXT.get("model")
+
+        if eval_fn is None or mdl is None:
+            raise RuntimeError(
+                "Direct evaluation requested, but the evaluation function or model "
+                "was not provided. Set evaluation=... in set_ema_context(...)."
+            )
+
+        base_kwargs = {c.name: c.value for c in getattr(mdl, "constants", [])}
+
+        ref = _EMA_CONTEXT.get("reference")
+        if ref is not None and hasattr(ref, "ssp_rcp_scenario"):
+            base_kwargs["ssp_rcp_scenario"] = ref.ssp_rcp_scenario
+        else:
+            idx = _EMA_CONTEXT.get("reference_index")
+            if idx is not None:
+                base_kwargs["ssp_rcp_scenario"] = idx
+
+        if self._lever_names is None or len(self._lever_names) != len(casted_vars):
+            raise RuntimeError(
+                f"Lever count mismatch: nvars={len(casted_vars)} "
+                f"vs lever_names={len(self._lever_names) if self._lever_names else None}"
+            )
+
+        lever_map = {name: val for name, val in zip(self._lever_names, casted_vars)}
+        kwargs = {**base_kwargs, **lever_map}
+
+        raw = eval_fn(**kwargs)
+
+        # Determine objectives/constraints based on type/length
+        objectives = None
+        constraints = None
+
+        if isinstance(raw, tuple):
+            # If we have constraints, and the tuple matches (objs, constr),
+            # interpret accordingly. Otherwise, treat entire tuple as objectives.
+            if (
+                len(raw) == 2
+                and getattr(self.problem, "nconstr", 0) > 0
+                and isinstance(raw[1], (list, tuple, np.ndarray))
+            ):
+                objectives, constraints = raw
+            else:
+                objectives = raw
+        elif isinstance(raw, dict):
+            # Map by outcome names if provided
+            names = self._outcome_names or list(raw.keys())
+            objectives = [raw[name] for name in names]
+        else:
+            objectives = raw
+
+        # Convert to numpy array for convenience, then to float list
+        def _to_float_list(x):
+            arr = np.asarray(x, dtype=float).flatten()
+            return arr.tolist()
+
+        objectives = _to_float_list(objectives) if objectives is not None else []
+
+        nconstr = getattr(self.problem, "nconstr", 0)
+        if nconstr:
+            if constraints is None:
+                constraints = [0.0] * nconstr
+            else:
+                constraints = _to_float_list(constraints)
+                if len(constraints) < nconstr:
+                    constraints += [0.0] * (nconstr - len(constraints))
+                else:
+                    constraints = constraints[:nconstr]
+            return objectives, constraints
+        else:
+            return objectives
+
+    # ------------------------------------------------------------------
+    def _make_callback(self):
+        """
+        Build the evaluation function Borg will call. If `direct_evaluation` is set,
+        we use _evaluate_directly; otherwise we fall back to Problem.evaluate.
+        """
+        problem = self.problem
+        direct = bool(self.direct_evaluation)
+
+        def cb(*x):
+            casted_vars = []
+            for val, t in zip(x, problem.types):
+                if isinstance(t, Integer):
+                    lo, hi = t.min_value, t.max_value
+                    v = int(round(val))
+                    if lo is not None:
+                        v = max(lo, v)
+                    if hi is not None:
+                        v = min(hi, v)
+                    casted_vars.append(v)
+                elif isinstance(t, Binary):
+                    casted_vars.append(1 if val >= 0.5 else 0)
+                else:
+                    lo = getattr(t, "min_value", None)
+                    hi = getattr(t, "max_value", None)
+                    v = float(val)
+                    if lo is not None:
+                        v = max(lo, v)
+                    if hi is not None:
+                        v = min(hi, v)
+                    casted_vars.append(v)
+
+            if direct:
+                return self._evaluate_directly(casted_vars)
+            else:
+                return self._evaluate_with_problem(casted_vars)
+
+        return cb
+
+    def _set_bounds(self, borg):
+        """Align Platypus bounds with Borg’s expectation."""
+        bounds = []
+        for t in self.problem.types:
+            if isinstance(t, Binary):
+                lo, hi = 0.0, 1.0
+            else:
+                lo = getattr(t, "min_value", None)
+                hi = getattr(t, "max_value", None)
+                if lo is None or hi is None:
+                    raise ValueError("All variables must have finite bounds for Borg.")
+            bounds.append([float(lo), float(hi)])
+        borg.setBounds(*bounds)
+
+    def run(self, max_evaluations: int):
+        """
+        Serial run for Borg (no MPI). Converts results to Platypus Solution objects.
+        """
+        from borg import Borg, Configuration
+
+        if self.borg_library_path:
+            Configuration.setBorgLibrary(self.borg_library_path)
+        if self.seed is not None:
+            Configuration.seed(self.seed)
+
+        nvars = self.problem.nvars
+        nobjs = self.problem.nobjs
+        nconstr = getattr(self.problem, "nconstr", 0)
+        callback = self._make_callback()
+        borg = Borg(nvars, nobjs, nconstr, callback)
+
+        self._set_bounds(borg)
+        borg.setEpsilons(*self.epsilons)
+
+        settings = dict(self.solve_settings)
+        settings["maxEvaluations"] = int(max_evaluations)
+        if self.population_size is not None:
+            settings.setdefault("initialPopulationSize", int(self.population_size))
+            settings.setdefault("minimumPopulationSize", int(self.population_size))
+
+        borg_result = borg.solve(settings)
+
+        self.result = []
+        if borg_result is not None:
+            for s_borg in borg_result:
+                sol = Solution(self.problem)
+                sol.variables = list(s_borg.getVariables())
+                sol.objectives = list(s_borg.getObjectives())
+                if nconstr:
+                    sol.constraints = list(s_borg.getConstraints())
+                self.result.append(sol)
+            self.nfe = settings["maxEvaluations"]
+        else:
+            self.nfe = 0
+
+        # Refresh stub so EMA convergence diagnostics still work
+        self.archive = _ArchiveView(self.result, improvements=len(self.result))
+        self.algorithm = _AlgorithmStub(self.archive)
+
+    def step(self):
+        """Newer Platypus requires a concrete step() method even if run() is used."""
+        return