the weights of elastic ties introduce
How strong need weak interactions (neighbour's neighbours) to be pooled to ensure a dynamic capacity for exogeneous shocks?
Using square positive definite matrices, can we find a sparse graph that indicate conditional dependence among the domain of a priori independent covariates estimating the maximum likelihood of potential interactions with or without a target matrix that restrict the search space to predefined characteristics of likely interactions, like just a diagonal, meaning having pairwise interactions and additive decomposition.
There is a weak correspondence between likelihood, estimated parameter variance and regularization to care less about multiplicative effects that could deform the shape of the parameter or explain non-linear interactions, i.e. more least squares. This is especially crucial for highdimensional data, where feature selection must be done carefully (as heavy tails can lie at the edge of stability, thus limitting performance gains (like just changes in exploration strategies in expectation maximization mode, but also "perturbation management" (super heavy tailed risk of exploitative adversarial probes) to mean field dynamics due to risk bifurcation), because it might kill a species that would otherwise outperform the arena. Therefore finding a balanced trade-off between L1 and L2 regularization is critical to understand a dynamic program and its different modes it can run efficiently on with different work load and queueing scenarios
tbc..