On learning agent-based models from data. (arXiv:2205.05052v2 [physics.soc-ph] UPDATED)

Agent-Based Models (ABMs) are used in several fields to study the evolution
of complex systems from micro-level assumptions. However, ABMs typically can
not estimate agent-specific (or "micro") variables: this is a major limitation
which prevents ABMs from harnessing micro-level data availability and which
greatly limits their predictive power. In this paper, we propose a protocol to
learn the latent micro-variables of an ABM from data. The first step of our
protocol is to reduce an ABM to a probabilistic model, …

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