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Re-Optimizing Food Systems: A Dynamic Analysis of Production, Sustainability, and Equity

Authors: Anthony Artino and Sarah Decipeda
Institution: Kent State University, Department of Mathematical Sciences
Course: Modeling Projects (MATH 52039)

Project Overview

In the modern food systems, they prioritize production efficiency and economic profit that are quite often at the direct expense of environmental health and equitable distribution. This research project models agriculture as a highly dynamic system as it analyzes the mathematical tension between raw economic growth, ecological collapse, and social fairness.

By using Ordinary Differential Equations (ODEs) and Stochastic Partial Differential Equations (SPDEs), this project simulates the long-term viability of the food architecture. We apply dynamic feedback loops and hypothetical government policies to adjust and improve the systems all across three different distinctive state profiles.

  • Louisiana: High food insecurity
  • Connecticut: Average food insecurity
  • New Hampshire: Low food insecurity

Interactive Web Platform (Model Playground)

This repository hosts a live, interactive "Model Playground" through GitHub Pages. Any user can directly engage with the simulations, adjusting intervention parameters, and viewing real-time systemic outcomes directly in their browser.

[Link to Live Project Website](https://antha21.github.io/re-optimizing-food-systems/)

The Mathematical Framework

The interactive models and underlying Python architecture are driven by four state variables:

  1. Food Production Dynamics (F): The agricultural biomass generated to meet population demands.
  2. Natural Resource Health (N): The limiting biological factor, tracking soil fertility, water availability, and ecological decay.
  3. Economic Capital (C): The available wealth that is utilized to fund agricultural operations, sustainability initiatives, and equity programs.
  4. Equity Index (Eq): A measurement of the fairness of food distribution as well as general accessibility.

Simulation Scenarios

  • General ODE Models: The baseline mathematical foundation observing the food system without government intervention.
  • Immediate Intervention: A theoretical benchmark that measures the maximum possible effectiveness of instantaneous crisis-response policies.
  • Political Delay: A realistic model that introduces human friction, where legislative drafting, public skepticism, and lobbying can create time-lagged responses to ecological and social crises.
  • Spatial Considerations & SPDE Modeling: A 3D simulation that tracks the physical flow of food and capital across a geographic landscape (a 100-mile highway route) while subjecting the system to unpredictable environmental and market shocks (Geometric Brownian Motion).

Standalone Bonus Section: ODE 3D

Included in the Model Playground is ODE 3D, an interactive First-Person Shooter (FPS) and scavenger hunt game.

Note: This game is a standalone bonus section independent of the general models' simulations. It focuses solely on the mathematical mechanics of Ordinary Differential Equations. Players can select between 4 different levels and navigate a full 3D environment to solve equation-based challenges.

About

A bioeconomic mathematical modeling project simulating food production, natural resource health, economic capital, and equity. Analyzes immediate and delayed policy interventions using ODEs and SPDEs in Python, featuring an interactive web platform and a standalone ODEs 3D FPS game.

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