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

Author

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

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. In economics, ABM has enriched our understanding of markets, industrial organization, labor, macro, development, environmental and resource economics, as well as policy. 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 hurdles that must be overcome to achieve this.

Suggested Citation

  • Farmer, J. Doyne & Axtell, Robert L., 2022. "Agent-Based Modeling in Economics and Finance: Past, Present, and Future," INET Oxford Working Papers 2022-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    • Handle: RePEc:amz:wpaper:2022-10
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    File URL: https://www.inet.ox.ac.uk/files/JEL-v2.0.pdf
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    Citations

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    Cited by:

    1. Arthur, W. Brian, 2023. "Economics in nouns and verbs," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 638-647.
    2. Alessandro Caiani & Ermanno Catullo, 2023. "Fiscal Transfers and Common Debt in a Monetary Union: A Multi-Country Agent Based-Stock Flow Consistent Model," LEM Papers Series 2023/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized Neural Networks for Agent-Based Model Forecasting," Bank of Russia Working Paper Series wps115, Bank of Russia.
    4. Michele Vodret & Iacopo Mastromatteo & Bence Tóth & Michael Benzaquen, 2023. "Microfounding GARCH models and beyond: a Kyle-inspired model with adaptive agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 599-625, July.
    5. Aaron Wheeler & Jeffrey D. Varner, 2023. "Scalable Agent-Based Modeling for Complex Financial Market Simulations," Papers 2312.14903, arXiv.org, revised Jan 2024.
    6. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
    7. Moreno-Casas, Vicente & Espinosa, Victor I. & Wang, William Hongsong, 2022. "The political economy of complexity: The case of cyber-communism," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 566-580.
    8. Xinyu Li, 2022. "The impact of moving expenses on social segregation: a simulation with RL and ABM," Papers 2211.12475, arXiv.org.
    9. Benjamin Patrick Evans & Sumitra Ganesh, 2024. "Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning," Papers 2402.00787, arXiv.org.
    10. Aldo Glielmo & Marco Favorito & Debmallya Chanda & Domenico Delli Gatti, 2023. "Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs," Papers 2302.11835, arXiv.org, revised Dec 2023.
    11. Ștefan Ionescu & Nora Chiriță & Ionuț Nica & Camelia Delcea, 2023. "An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans," Sustainability, MDPI, vol. 15(15), pages 1-32, August.
    12. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    13. Namid R. Stillman & Rory Baggott & Justin Lyon & Jianfei Zhang & Dingqiu Zhu & Tao Chen & Perukrishnen Vytelingum, 2023. "Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks," Papers 2311.11913, arXiv.org, revised Nov 2023.
    14. Jialin Dong & Kshama Dwarakanath & Svitlana Vyetrenko, 2023. "Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents," Papers 2311.17252, arXiv.org.
    15. Richiardi, Matteo & Bronka, Patryk & van de Ven, Justin, 2023. "Back to the future: Agent-based modelling and dynamic microsimulation," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA8/23, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    16. Marco Catola & Silvia Leoni, 2023. "Pollution Abatement and Lobbying in a Cournot Game. An Agent-Based Modelling approach," Discussion Papers 2023/294, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    17. Haochong Xia & Shuo Sun & Xinrun Wang & Bo An, 2023. "Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context," Papers 2309.07708, arXiv.org, revised Feb 2024.
    18. Nicolas Cofre & Magdalena Mosionek-Schweda, 2023. "A simulated electronic market with speculative behaviour and bubble formation," Papers 2311.12247, arXiv.org.

    More about this item

    Keywords

    agent-based computational economics; multi-agent systems; agent-based modeling and simulation; distributed systems;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • D00 - Microeconomics - - General - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • G00 - Financial Economics - - General - - - General

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