Corrado Monti, Marco Pangallo, Gianmarco De Francisci Morales, Francesco Bonchi
arXiv 10 May 2022 · physics.soc-ph · publishedScientific Reports (2023) · 36 citations (OpenAlex)
arXiv:2205.05052 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Marco Pangallo, Jean-Pierre Nadal, and Annick Vignes (2019) Residential income segregation: A behavioral model of the housing market self | 1.000 | 6 | 3 | 100% |
| 2 | Thomas Lux (2018) Estimation of agent-based models using sequential monte carlo methods | 0.811 | 4 | 2 | 100% |
| 3 | Robert Clay, Jonathan A Ward, Patricia Ternes, Le-Minh Kieu, and Nic… (2021) Real-time agent-based crowd simulation with the reversible jump unscented kalman filter | 0.737 | 3 | 2 | 100% |
| 4 | Tadeo Javier Cocucci, Manuel Pulido, Juan Pablo Aparicio, Juan Ruź,… (2022) Inference in epidemiological agent-based models using ensemble-based data assimilation | 0.737 | 3 | 2 | 100% |
| 5 | Jonathan A Ward, Andrew J Evans, and Nicolas S Malleson (2016) Dynamic calibration of agent-based models using data assimilation | 0.737 | 3 | 2 | 100% |
| 6 | Domenico Delli Gatti and Jakob Grazzini (2020) Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic agent based models | 0.644 | 2 | 2 | 100% |
| 7 | Michele Loberto, Andrea Luciani, and Marco Pangallo (2022) What do online listings tell us about the housing market? self | 0.644 | 2 | 2 | 100% |
| 8 | Corrado Monti, Gianmarco De Francisci Morales, and Francesco Bonchi (2020) Learning Opinion Dynamics from Social Traces self | 0.644 | 2 | 2 | 100% |
| 9 | Michael I Jordan et al (2004) Graphical models | 0.511 | 2 | 1 | 100% |
| 10 | Paul Windrum, Giorgio Fagiolo, and Alessio Moneta (2007) Empirical validation of agent-based models: Alternatives and prospects | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 26 scored citations.