Abstract

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world microdata and whether the ABMs dynamics track empirical time series. This chapter discusses how making ABMs data-driven helps overcome limitations of traditional ABMs and makes ABMs a stronger alternative to equilibrium models. We review state-of-the-art methods in parameter calibration, initialization, and data assimilation, and then present successful applications that have generated new scientific knowledge and informed policy decisions. This chapter serves as a manifesto for data-driven ABMs, introducing a definition and classification and outlining the state of the field, and as a guide for those new to the field.

Bibliography

Anand, K., I. van Lelyveld, Á. Banai, S. Friedrich, R. Garratt, G. Hałaj, J. Fique, et al. 2018. “The Missing Links: A Global Study on Uncovering Financial Network Structures from Partial Data.” Journal of Financial Stability 35:107–119. https://doi.org/10.1016/j.jfs.2017.05.012.

Arentze, T., H. Timmermans, and F. Hofman. 2007. “Creating Synthetic Household Populations: Problems and Approach.” Transportation Research Record 2014 (1): 85–91. https://doi.org/10.3141/2014-11.

Artinger, F. M., G. Gigerenzer, and P. Jacobs. 2020. Labor Provision under Uncertainty. Available at SSRN 3728515.

Artinger, Florian M., Gerd Gigerenzer, and Perke Jacobs. 2022. “Satisficing: Integrating Two Traditions.” Journal of Economic Literature 60 (2): 598–635.

Athey, S. 2018. “The Impact of Machine Learning on Economics.” In The Economics of Artificial Intelligence: An Agenda, 507–547. Chicago, IL: University of Chicago Press.

Atkinson, A. B. 2009. “Economics as a Moral Science.” Economica 76:791–804. https://doi.org/10.1111/j.1468-0335.2009.00788.x.

Axtell, R. L., and J. D. Farmer. 2024. “Agent-Based Modeling in Economics and Finance: Past, Present, and Future.” Journal of Economic Literature, 1–101. https://doi.org/10.1257/jel.20221319.

Axtell, R. L., J. D. Farmer, J. Geanakoplos, P. Howitt, E. Carrella, B. Conlee, J. Goldstein, et al. 2014. An Agent-Based Model of the Housing Market Bubble in Metropolitan Washington, DC. Available at SSRN 4710928.

Axtell, R. L., O. A. Guerrero, and E. López. 2019. “Frictional Unemployment on Labor Flow Networks.” Journal of Economic Behavior & Organization 160:184–201. https://doi.org/10.1016/j.jebo.2019.02.028.

Barde, S. 2017. “A Practical, Accurate, Information Criterion for Nth Order Markov Processes.” Computational Economics 50 (2): 281–324. https://doi.org/10.1007/s10614-016-9617-9.

Benedetti, M., G. Catapano, F. De Sclavis, M. Favorito, A. Glielmo, D. Magnanimi, and A. Muci. 2022. “Black-It: A Ready-to-Use and Easy-to-Extend Calibration Kit for Agent-Based Models.” Journal of Open Source Software 7 (79): 4622. https://doi.org/10.21105/joss.04622.

Bergmann, B. R. 1974. “A Microsimulation of the Macroeconomy with Explicitly Represented Money Flows.” In Annals of Economic and Social Measurement, edited by S. V. Berg, 3:475–489. 3. Cambridge, MA: NBER. https://www.nber.org/system/files/chapters/c10173/c10173.pdf.

Berryman, A. K., J. Bü̈cker, F. Senra de Moura, P. Barbrook-Johnson, M. Hanusch, P. Mealy, J. D. Farmer, and R. M. del Rio-Chanona. 2025. “Skill and Spatial Mismatches for Sustainable Development Pathways in Brazil.” arXiv:2503.05310, https://doi.org/10.48550/arXiv.2503.05310.

Bonabeau, E. 2002. “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems.” Proceedings of the National Academy of Sciences 99 (suppl 3): 7280–7287. https://doi.org/10.1073/pnas.082080899.

Bonfiglio, A., and F. Chelli. 2008. “Assessing the Behaviour of Non-Survey Methods for Constructing Regional Input–Output Tables through a Monte Carlo Simulation.” Economic Systems Research 20 (3): 243–258. https://doi.org/10.1080/09535310802344315.

Borgonovo, E., M. Pangallo, J. Rivkin, L. Rizzo, and N. Siggelkow. 2022. “Sensitivity Analysis of Agent-Based Models: A New Protocol.” Computational and Mathematical Organization Theory 28 (1): 52–94. https://doi.org/10.1007/s10588-021-09358-5.

Borsos, A., A. Carro, A. Glielmo, M. Hinterschweiger, J. Kaszowska-Mojsa, and A. Uluc. 2025. “Agent-Based Modeling at Central Banks: Recent Developments and New Challenges.” In The Economy as an Evolving Complex System IV, Santa Fe, NM: SFI Press.

Brock, W. A., and C. H. Hommes. 1998. “Heterogeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model.” Journal of Economic Dynamics and Control 22 (8–9): 1235–1274. https://doi.org/10.1016/S0165-1889(98)00011-6.

Carrassi, A., M. Bocquet, L. Bertino, and G. Evensen. 2018. “Data Assimilation in the Geosciences: An Overview of Methods, Issues, and Perspectives.” Wiley Interdisciplinary Reviews: Climate Change 9 (5): e535. https://doi.org/10.1002/wcc.535.

Carrella, E. 2021. “No Free Lunch when Estimating Simulation Parameters.” Journal of Artificial Societies and Social Simulation 24 (2). https://doi.org/10.18564/jasss.4572.

Casert, C., I. Tamblyn, and S. Whitelam. 2024. “Learning Stochastic Dynamics and Predicting Emergent Behavior Using Transformers.” Nature Communications 15 (1): 1875. https://doi.org/10.1038/s41467-024-45629-w.

Cazcarro, I., A. F. Amores, I. Arto, and K. Kratena. 2022. “Linking Multisectoral Economic Models and Consumption Surveys for the European Union.” Economic Systems Research 34 (1): 22–40. https://doi.org/10.1080/09535314.2020.1856044.

Colon, C., S. Hallegatte, and J. Rozenberg. 2021. “Criticality Analysis of a Country’s Transport Network via an Agent-Based Supply Chain Model.” Nature Sustainability 4 (3): 209–215. https://doi.org/10.1038/s41893-020-00649-4.

Coyle, D. 2021. Cogs and Monsters: What Economics Is, and What It Should Be. Princeton, NJ: Princeton University Press.

Cozzi, F., M. Pangallo, A. Perotti, A. Panisson, and C. Monti. 2024. “Neural Surrogates to Learn Agent-Based Models Micro-Dynamics.” In preparation.

Dawid, Herbert, and Domenico Delli Gatti. 2018. “Agent-based macroeconomics.” In Handbook of Computational Economics, 4:63–156. Elsevier.

de Marchi, S., and S. E. Page. 2014. “Agent-Based Models.” Annual Review of Political Science 17 (1): 1–20. https://doi.org/10.1146/annurev-polisci-080812-191558 .

del Rio-Chanona, R. M., M. Frank, P. Mealy, E. Moro, and L. Nedelkoska. 2025. “Enhancing Labor Markets’ Resilience in Preparation for the Net-Zero Transition and New Technologies through Complex Systems.” In The Economy as an Evolving Complex System IV, Santa Fe, NM: SFI Press.

del Rio-Chanona, R. M., P. Mealy, M. Beguerisse-Díaz, F. Lafond, and J. D. Farmer. 2021. “Occupational Mobility and Automation: A Data-Driven Network Model.” Journal of the Royal Society Interface 18 (174): 20200898. https://doi.org/10.1098/rsif.2020.0898.

del Rio-Chanona, R. M., M. Pangallo, P. Mishkin, and C. Hommes. 2024. “Market Dynamics of Price Expectations with Generative AI Agents.” In preparation.

Delli Gatti, D., G. Fagiolo, M. Gallegati, M. Richiardi, and A. Russo. 2018. Agent-Based Models in Economics: A Toolkit. Cambridge, UK: Cambridge University Press.

Dyer, J., P. Cannon, J. D. Farmer, and S. M. Schmon. 2024. “Black-Box Bayesian Inference for Agent-Based Models.” Journal of Economic Dynamics and Control 161:104827. https://doi.org/10.1016/j.jedc.2024.104827.

Epstein, Joshua M., and Robert Axtell. 1996. Growing Artificial Societies: Social Science from the Bottom Up. Brookings Institution Press.

Erev, I., E. Ert, O. Plonsky, D. Cohen, and O. Cohen. 2017. “From Anomalies to Forecasts: Toward a Descriptive Model of Decisions under Risk, under Ambiguity, and from Experience.” Psychological Review 124 (4): 369. https://doi.org/10.1037/rev0000062.

Fair, K. R., and O. A. Guerrero. 2025. “Emerging Labour Flow Networks.” EPJ Data Science 14 (1). https://doi.org/10.1140/epjds/s13688-025-00539-9.

Farmer, J. D. 2024. Making Sense of Chaos: A Better Economics for a Better World. London, UK: Penguin.

Filatova, T. 2015. “Empirical Agent-Based Land Market: Integrating Adaptive Economic Behavior in Urban Land-Use Models.” Computers, Environment and Urban Systems 54:397–413. https://doi.org/10.1016/j.compenvurbsys.2014.06.007.

Franke, R. 2009. “Applying the Method of Simulated Moments to Estimate a Small Agent-Based Asset Pricing Model.” Journal of Empirical Finance 16 (5): 804–815. https://doi.org/10.1016/j.jempfin.2009.06.006.

Gabaix, X. 2019. “Behavioral Inattention.” In Handbook of Behavioral Economics: Applications and Foundations 1, 2:261–343. https://doi.org/10.1016/bs.hesbe.2018.11.001.

Geanakoplos, J., R. L. Axtell, J. D. Farmer, P. Howitt, B. Conlee, J. Goldstein, M. Hendrey, N. M. Palmer, and C.-Y. Yang. 2012. “Getting at Systemic Risk via an Agent-Based Model of the Housing Market.” American Economic Review 102 (3): 53–58. https://doi.org/10.1257/aer.102.3.53.

Glielmo, A., M. Favorito, D. Chanda, and D. Delli Gatti. 2023. “Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs.” In Proceedings of the Fourth ACM International Conference on AI in Finance, 305–313. New York, NY: Association for Computing Machinery.

Grattarola, D., L. Livi, and C. Alippi. 2021. “Learning Graph Cellular Automata.” In NeurIPS ’21: Proceedings of the 35th International Conference on Neural Information Processing Systems, edited by M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan, 34:20983–20994.

Grazzini, J., M. G. Richiardi, and M. Tsionas. 2017. “Bayesian Estimation of Agent-Based Models.” Journal of Economic Dynamics and Control 77:26–47. https://doi.org/10.1016/j.jedc.2017.01.014.

Guerini, M., and A. Moneta. 2017. “A Method for Agent-Based Models Validation.” Journal of Economic Dynamics and Control 82:125–141. https://doi.org/10.1016/j.jedc.2017.06.001.

Haer, T., W. J. Wouter Botzen, H. de Moel, and J. C. J. H. Aerts. 2017. “Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent-Based Model Approach.” Risk Analysis 37 (10): 1977–1992. https://doi.org/10.1111/risa.12740.

Hallegatte, S. 2008. “An Adaptive Regional Input–Output Model and Its Application to the Assessment of the Economic Cost of Katrina.” Risk Analysis 28 (3): 779–799. https://doi.org/10.1111/j.1539-6924.2008.01046.x.

Harstad, R. M., and R. Selten. 2013. “Bounded-Rationality Models: Tasks to Become Intellectually Competitive.” Journal of Economic Literature 51 (2): 496–511. https://doi.org/10.1257/jel.51.2.496.

Hommes, C. 2021. “Behavioral and Experimental Macroeconomics and Policy Analysis: A Complex Systems Approach.” Journal of Economic Literature 59 (1): 149–219. https://doi.org/10.1257/jel.20191434.

Hommes, C., S. Kozicki, S. Poledna, and Y. Zhang. 2025. “How an Agent-Based Model Can Support Monetary Policy in a Complex Evolving Economy.” In The Economy as an Evolving Complex System IV, edited by J. Bednar, E. Beinhocker, M. del Rio-Chanona, J. D. Farmer, J. Kaszowska-Mojsa, F. Lafond, P. Mealy, M. Pangallo, and A. Pichler. Santa Fe, NM: SFI Press.

Horton, J. J. 2023. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? Technical report. National Bureau of Economic Research. https://doi.org/10.3386/w31122.

Ialongo, L. N., C. de Valk, E. Marchese, F. Jansen, H. Zmarrou, T. Squartini, and D. Garlaschelli. 2022. “Reconstructing Firm-Level Interactions in the Dutch Input–Output Network from Production Constraints.” Scientific Reports 12 (1): 11847. https://doi.org/10.1038/s41598-022-13996-3.

Inoue, H., and Y. Todo. 2019. “Firm-Level Propagation of Shocks through Supply-Chain Networks.” Nature Sustainability 2 (9): 841–847. https://doi.org/10.1038/s41893-019-0351-x.

Jennings, N. R. 2000. “On Agent-Based Software Engineering.” Artificial Intelligence 117 (2): 277–296. https://doi.org/10.1016/S0004-3702(99)00107-1.

Kaszowska-Mojsa, J., and M. Pipień. 2020. “Macroprudential Policy in a Heterogeneous Environment—An Application of Agent-Based Approach in Systemic Risk Modelling.” Entropy 22 (2): 129. https://doi.org/10.3390/e22020129.

Kukacka, J., and J. Barunik. 2017. “Estimation of Financial Agent-Based Models with Simulated Maximum Likelihood.” Journal of Economic Dynamics and Control 85:21–45. https://doi.org/10.1016/j.jedc.2017.09.006.

Lamperti, F., A. Roventini, and A. Sani. 2018. “Agent-Based Model Calibration Using Machine Learning Surrogates.” Journal of Economic Dynamics and Control 90:366–389. https://doi.org/10.1016/j.jedc.2018.03.011.

Li, J., and C. O'Donoghue. 2013. “A Survey of Dynamic Microsimulation Models: Uses, Model Structure and Methodology.” International Journal of Microsimulation 6 (2): 3–55. https://doi.org/10.34196/ijm.00082.

Lux, T. 2018. “Estimation of Agent-Based Models Using Sequential Monte Carlo Methods.” Journal of Economic Dynamics and Control 91:391–408. https://doi.org/10.1016/j.jedc.2018.01.021.

Markhvida, M., B. Walsh, S. Hallegatte, and J. Baker. 2020. “Quantification of Disaster Impacts through Household Well-Being Losses.” Nature Sustainability 3 (7): 538–547. https://doi.org/10.1038/s41893-020-0508-7.

Mérő, B., A. Borsos, Z. Hosszú, Z. Oláh, and N. Vágó. 2023. “A High-Resolution, Data-Driven Agent-Based Model of the Housing Market.” Journal of Economic Dynamics and Control 155:104738. https://doi.org/10.1016/j.jedc.2023.104738.

Monti, C., G. De Francisci Morales, and F. Bonchi. 2020. “Learning Opinion Dynamics from Social Traces.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 764–773. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3394486.3403119.

Monti, C., M. Pangallo, G. De Francisci Morales, and F. Bonchi. 2023. “On Learning Agent-Based Models from Data.” Scientific Reports 13 (1): 9268. https://doi.org/10.1038/s41598-023-35536-3.

Muellbauer, J. 2020. “The Coronavirus Pandemic and US Consumption.” VoxEU.org, 11 April. https://voxeu.org/article/coronavirus-pandemic-and-us-consumption.

Mungo, L., F. Lafond, P. Astudillo-Estévez, and J. D. Farmer. 2023. “Reconstructing Production Networks Using Machine Learning.” Journal of Economic Dynamics and Control 148:104607. https://doi.org/10.1016/j.jedc.2023.104607.

Muth, J. F. 1961. “Rational Expectations and the Theory of Price Movements.” Econometrica 29:315–335. https://doi.org/10.2307/1909635.

Oswald, K., Y. Suchak, and N. Malleson. 2025. “Agent-Based Models of the United States Wealth Distribution with Ensemble Kalman Filter.” Journal of Economic Behavior & Organization 229:106820. https://doi.org/10.1016/j.jebo.2024.106820.

Otto, C., S. N. Willner, L. Wenz, K. Frieler, and A. Levermann. 2017. “Modeling Loss-Propagation in the Global Supply Network: The Dynamic Agent-Based Model Acclimate.” Journal of Economic Dynamics and Control 83:232–269. https://doi.org/10.1016/j.jedc.2017.08.001.

Pangallo, M. 2025. “Equations vs. Maps: Complexity, Equilibrium, Disequilibrium.” In The Economy as an Evolving Complex System IV, edited by J. Bednar, E. Beinhocker, M. del Rio-Chanona, J. D. Farmer, J. Kaszowska-Mojsa, F. Lafond, P. Mealy, M. Pangallo, and A. Pichler. Santa Fe, NM: SFI Press.

Pangallo, M., A. Aleta, R. M. del Rio-Chanona, A. Pichler, D. Martín-Corral, M. Chinazzi, F. Lafond, et al. 2023. “The Unequal Effects of the Health–Economy Trade-Off during the COVID-19 Pandemic.” Nature Human Behaviour 1–12. https://doi.org/10.1038/s41562-023-01747-x.

Pangallo, M., M. Coronese, F. Lamperti, G. Cervone, and F. Chiaromonte. 2024. “Climate-Change Attitudes in a Data-Driven Agent-Based Model of the Housing Market.” In preparation.

Papadopoulos, G. 2019. “Income Inequality, Consumption, Credit and Credit Risk in a Data-Driven Agent-Based Model.” Journal of Economic Dynamics and Control 104:39–73. https://doi.org/10.1016/j.jedc.2019.05.002.

Pichler, A., M. Pangallo, R. M. del Rio-Chanona, F. Lafond, and J. D. Farmer. 2022. “Forecasting the Propagation of Pandemic Shocks with a Dynamic Input–Output Model.” Journal of Economic Dynamics and Control 104527. https://doi.org/10.1016/j.jedc.2022.104527.
Platt, D. 2020. “A Comparison of Economic Agent-Based Model Calibration Methods.” Journal of Economic Dynamics and Control 113:103859. https://doi.org/10.1016/j.jedc.2020.103859.

Poledna, S., M. G. Miess, C. Hommes, and K. Rabitsch. 2023. “Economic Forecasting with an Agent-Based Model.” European Economic Review 151:104306. https://doi.org/10.1016/j.euroecorev.2022.104306.

Quera-Bofarull, A., J. Dyer, A. Calinescu, J. D. Farmer, and M. Wooldridge. 2023. “BlackBIRDS: Black-Box Inference for Differentiable Simulators.” Journal of Open Source Software 8 (89). https://doi.org/10.21105/joss.05776.

Richiardi, M., Justin van de Ven, and P. Bronka. 2025. “Back to the Future: Agent-Based Modeling and Dynamic Microsimulation.” In The Economy as an Evolving Complex System IV, Santa Fe, NM: SFI Press.

Roos, M. W. M. 2018. Values, Attitudes and Economic Behavior, 777. Ruhr Economic Papers.

Schelling, Thomas C. 1971. “Dynamic Models of Segregation.” Journal of Mathematical Sociology 1 (2): 143–186.

Turrell, A. 2025. “Cutting through Complexity: Data Science and How It Can Help Policymakers Understand the World.” In The Economy as an Evolving Complex System IV, Santa Fe, NM: SFI Press.

Ward, J. A., A. J. Evans, and N. S. Malleson. 2016. “Dynamic Calibration of Agent-Based Models Using Data Assimilation.” Royal Society Open Science 3 (4): 150703. https://doi.org/10.1098/rsos.150703.

BACK TO The Economy as an Evolving Complex System IV