# On learning agent-based models from data

arXiv: https://arxiv.org/abs/2205.05052 · HTML: https://mlsanity.com/paper/2205.05052/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 3 of 3)

- [Automatic differentiation in ML: Where we are and where we should be going](https://mlsanity.com/paper/1810.11530/) (2018)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [Approximate Bayesian Computation with Path Signatures](https://mlsanity.com/paper/2106.12555/) (2021)

## Built on by (most cited first, 14 of 14)

- [On the limits of agency in agent-based models](https://mlsanity.com/paper/2409.10568/) (2024)
- [MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework](https://mlsanity.com/paper/2504.21582/) (2025)
- [Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs](https://mlsanity.com/paper/2302.11835/) (2023)
- [Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling](https://mlsanity.com/paper/2405.02161/) (2024)
- [Bayesian calibration of differentiable agent-based models](https://mlsanity.com/paper/2305.15340/) (2023)
- [Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks](https://mlsanity.com/paper/2505.21426/) (2025)
- [Variational Inference of Parameters in Opinion Dynamics Models](https://mlsanity.com/paper/2403.05358/) (2024)
- [Bias and Identifiability in the Bounded Confidence Model](https://mlsanity.com/paper/2506.11751/) (2025)
- [STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics](https://mlsanity.com/paper/2507.11660/) (2025)
- [Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models](https://mlsanity.com/paper/2509.17625/) (2025)
- [A differentiable model of supply-chain shocks](https://mlsanity.com/paper/2511.05231/) (2025)
- [Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model](https://mlsanity.com/paper/2602.15572/) (2026)
- [SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization](https://mlsanity.com/paper/2604.17351/) (2026)
- [TI2PS: A Topology-Informed Inverse Design Framework for Stochastic Multicellular Pattern Formation](https://mlsanity.com/paper/2608.27931/) (2026)

## Abstract (arXiv)

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.