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Agent-Based Modeling in Economics and Finance: Past, Present, and Future

Author

Listed:
  • Robert L. Axtell
  • J. Doyne Farmer

Abstract

Agent-based modeling (ABM) is a novel computational methodology for representing the behavior of individuals in order to study social phenomena. Its use is rapidly growing in many fields. We review ABM in economics and finance and highlight how it can be used to relax conventional assumptions in standard economic models. ABM has enriched our understanding of markets, industrial organization, labor, macro, development, public policy, and environmental economics. In financial markets, substantial accomplishments include understanding clustered volatility, market impact, systemic risk, and housing markets. We present a vision for how ABMs might be used in the future to build more realistic models of the economy and review some of the hurdles that must be overcome to achieve this.

Suggested Citation

  • Robert L. Axtell & J. Doyne Farmer, 2025. "Agent-Based Modeling in Economics and Finance: Past, Present, and Future," Journal of Economic Literature, American Economic Association, vol. 63(1), pages 197-287, March.
    • Handle: RePEc:aea:jeclit:v:63:y:2025:i:1:p:197-287
    DOI: 10.1257/jel.20221319
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    Citations

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    Cited by:

    1. Karolina Safarzynska & Taras Kryvyy, 2025. "Integrating metals and minerals into climate-economic models: a review," Climatic Change, Springer, vol. 178(7), pages 1-23, July.
    2. Aurélien Goutsmedt & Francesco Sergi & Juan Acosta, 2025. "Economists, Economic Knowledge, and Central Banks," Working Papers hal-05083645, HAL.
    3. Matthew O. Jackson & Qiaozhu Me & Stephanie W. Wang & Yutong Xie & Walter Yuan & Seth Benzell & Erik Brynjolfsson & Colin F. Camerer & James Evans & Brian Jabarian & Jon Kleinberg & Juanjuan Meng & Se, 2025. "AI Behavioral Science," Papers 2509.13323, arXiv.org.
    4. Martin Jaraiz, 2026. "Macroeconomic Forecasting from Input-Output Tables Alone: A Darwinian Agent-Based Approach with FIGARO Data," Papers 2603.12412, arXiv.org, revised Mar 2026.
    5. Marcello Nieddu & Marco Raberto & Andrea Teglio, 2025. "The importance of being many: dynamics, interaction and aggregation in a multi-sector economy," Working Papers 2025: 04, Department of Economics, University of Venice "Ca' Foscari".
    6. Sáenz-Royo, Carlos & Hermoso, Ramón & Chiclana, Francisco, 2025. "Wind of change for agent decisions and innovation diffusion: The ASPID predictive model for technology adoption," Socio-Economic Planning Sciences, Elsevier, vol. 102(C).
    7. Federico Gabriele & Aldo Glielmo & Marco Taboga, 2025. "Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning," Papers 2510.12272, arXiv.org, revised Feb 2026.
    8. Ștefan Ionescu & Camelia Delcea & Ionuț Nica & Gabriel Dumitrescu & Claudiu-Emanuel Simion & Liviu-Adrian Cotfas, 2025. "Exploring Complexity: A Bibliometric Analysis of Agent-Based Modeling in Finance and Banking," IJFS, MDPI, vol. 13(2), pages 1-37, April.
    9. Sabrina Aufiero & Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2025. "Mapping Microscopic and Systemic Risks in TradFi and DeFi: a literature review," Papers 2508.12007, arXiv.org.
    10. Klügl, Franziska & Nordås, Hildegunn Kyvik, 2025. "Cross-border data flows and AI adoption: Agent-based model simulations," Structural Change and Economic Dynamics, Elsevier, vol. 75(C), pages 676-688.
    11. Andrea Caravaggio & Silvia Leoni, 2025. "An Agent-Based approach to high-cost drugs for infectious diseases," Working Papers - Economics wp2025_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    12. Kremer, Elise & Reissl, Severin & Fierro, Luca E. & Emmerling, Johannes & Lamperti, Francesco & Roventini, Andrea, 2025. "Energy price shocks in the European Union: Macroeconomic impacts, distributional effects and policy responses," Energy Economics, Elsevier, vol. 152(C).
    13. Mourad Messaadia & Mohammed Mispah Said Omar & Fouad Ben Abdelaziz, 2025. "Modeling Firm Growth Dynamics: The Role of Financing Strategies, Financial Management, and Managerial Skills through Multiagent Simulation," Annals of Data Science, Springer, vol. 12(6), pages 1825-1849, December.
    14. Gardini, Laura & Radi, Davide & Schmitt, Noemi & Sushko, Iryna & Westerhoff, Frank, 2025. "On the limits of informationally efficient stock markets: New insights from a chartist-fundamentalist model," International Review of Financial Analysis, Elsevier, vol. 105(C).
    15. Pastushkov, Aleksei & Boulatov, Alexei, 2025. "Pseudo-collusion in a centralized algorithmic financial market," Finance Research Letters, Elsevier, vol. 83(C).

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D00 - Microeconomics - - General - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • G00 - Financial Economics - - General - - - General

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