On learning agent-based models from data

TL;DR

This paper introduces a method to infer agent-specific micro-variables in agent-based models from data by converting ABMs into probabilistic models and using likelihood maximization, enhancing their predictive power.

Contribution

It proposes a novel protocol for learning latent micro-variables in ABMs through probabilistic modeling and gradient-based inference, addressing a key limitation of traditional ABMs.

Findings

Accurately estimates latent variables in a housing market ABM

Preserves the overall behavior of the original ABM

Enables out-of-sample forecasting using inferred micro-variables

Abstract

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, characterized by a computationally tractable likelihood. This reduction follows two general design principles: balance of stochasticity and data availability, and replacement of unobservable discrete choices with differentiable approximations. Then, our protocol proceeds by maximizing the likelihood of the latent variables via a gradient-based expectation maximization algorithm. We demonstrate our protocol by applying it to an ABM of the housing market, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We demonstrate that the obtained model allows accurate estimates of the latent variables, while preserving the general behavior of the ABM. We also show that our estimates can be used for out-of-sample forecasting. Our protocol can be seen as an alternative to black-box data assimilation methods, that forces the modeler to lay bare the assumptions of the model, to think about the inferential process, and to spot potential identification problems.