IDEAS home Printed from https://ideas.repec.org/a/bla/sysdyn/v40y2024i1ne1761.html

Generative agent‐based modeling: an introduction and tutorial

Author

Listed:
  • Navid Ghaffarzadegan
  • Aritra Majumdar
  • Ross Williams
  • Niyousha Hosseinichimeh

Abstract

We discuss the emerging new opportunity for building feedback‐rich computational models of social systems using generative artificial intelligence. Referred to as generative agent‐based models (GABMs), such individual‐level models utilize large language models to represent human decision‐making in social settings. We provide a GABM case in which human behavior can be incorporated into simulation models by coupling a mechanistic model of human interactions with a pre‐trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision‐making. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Suggested Citation

  • Navid Ghaffarzadegan & Aritra Majumdar & Ross Williams & Niyousha Hosseinichimeh, 2024. "Generative agent‐based modeling: an introduction and tutorial," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    • Handle: RePEc:bla:sysdyn:v:40:y:2024:i:1:n:e1761
    DOI: 10.1002/sdr.1761
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sdr.1761
    Download Restriction: no
    File URL: https://libkey.io/10.1002/sdr.1761?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. C. J. Ancker & A. V. Gafarian, 1963. "Some Queuing Problems with Balking and Reneging. I," Operations Research, INFORMS, vol. 11(1), pages 88-100, February.
    2. Vince Barabba & Chet Huber & Fred Cooke & Nick Pudar & Jim Smith & Mark Paich, 2002. "A Multimethod Approach for Creating New Business Models: The General Motors OnStar Project," Interfaces, INFORMS, vol. 32(1), pages 20-34, February.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. John J. Horton & Apostolos Filippas & Benjamin S. Manning, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    5. John Sterman, 2018. "System dynamics at sixty: the path forward," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 5-47, January.
    6. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters, in: Generative Social Science Studies in Agent-Based Computational Modeling, Princeton University Press.
    7. Erling Moxnes, 2023. "Challenges for sustainability: misperceptions and misleading advice," System Dynamics Review, System Dynamics Society, vol. 39(3), pages 185-206, July.
    8. Philipp Wunderlich & Andreas Größler & Nicole Zimmermann & Jac A. M. Vennix, 2014. "Managerial influence on the diffusion of innovations within intra-organizational networks," System Dynamics Review, System Dynamics Society, vol. 30(3), pages 161-185, July.
    9. C. J. Ancker & A. V. Gafarian, 1963. "Some Queuing Problems with Balking and Reneging---II," Operations Research, INFORMS, vol. 11(6), pages 928-937, December.
    10. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    11. Argyle, Lisa P. & Busby, Ethan C. & Fulda, Nancy & Gubler, Joshua R. & Rytting, Christopher & Wingate, David, 2023. "Out of One, Many: Using Language Models to Simulate Human Samples," Political Analysis, Cambridge University Press, vol. 31(3), pages 337-351, July.
    12. John J. Horton & Apostolos Filippas & Benjamin S. Manning, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org, revised Feb 2026.
    13. Sumaiya Haque & Hesam Mahmoudi & Navid Ghaffarzadegan & Konstantinos Triantis, 2023. "Mental models, cognitive maps, and the challenge of quantitative analysis of their network representations," System Dynamics Review, System Dynamics Society, vol. 39(2), pages 152-170, April.
    14. Negar Darabi & Niyousha Hosseinichimeh, 2020. "System dynamics modeling in health and medicine: a systematic literature review," System Dynamics Review, System Dynamics Society, vol. 36(1), pages 29-73, January.
    15. Navid Ghaffarzadegan & Richard C. Larson, 2018. "SD meets OR: a new synergy to address policy problems," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 327-353, January.
    16. Neil Ferguson, 2007. "Capturing human behaviour," Nature, Nature, vol. 446(7137), pages 733-733, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Niyousha Hosseinichimeh & Aritra Majumdar & Ross Williams & Navid Ghaffarzadegan, 2024. "From text to map: a system dynamics bot for constructing causal loop diagrams," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    3. Jiangbo Yu, 2022. "An elementary mechanism for simultaneously modeling discrete decisions and decision times," System Dynamics Review, System Dynamics Society, vol. 38(3), pages 215-245, July.
    4. Hongshen Sun & Juanjuan Zhang, 2025. "From Model Choice to Model Belief: Establishing a New Measure for LLM-Based Research," Papers 2512.23184, arXiv.org.
    5. Koji Takahashi & Joon Suk Park, 2025. "Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology," IMES Discussion Paper Series 25-E-13, Institute for Monetary and Economic Studies, Bank of Japan.
    6. Hui Chen & Antoine Didisheim & Mohammad & Pourmohammadi & Luciano Somoza & Hanqing Tian, 2025. "A Financial Brain Scan of the LLM," Papers 2508.21285, arXiv.org, revised Feb 2026.
    7. Navid Ghaffarzadegan & Richard C. Larson, 2018. "SD meets OR: a new synergy to address policy problems," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 327-353, January.
    8. repec:osf:osfxxx:r3qng_v1 is not listed on IDEAS
    9. Jiaxin Liu & Yixuan Tang & Yi Yang & Kar Yan Tam, 2025. "Evaluating and Aligning Human Economic Risk Preferences in LLMs," Papers 2503.06646, arXiv.org, revised Sep 2025.
    10. George Gui & Seungwoo Kim, 2025. "Leveraging LLMs to Improve Experimental Design: A Generative Stratification Approach," Papers 2509.25709, arXiv.org.
    11. Aliya Amirova & Theodora Fteropoulli & Nafiso Ahmed & Martin R Cowie & Joel Z Leibo, 2024. "Framework-based qualitative analysis of free responses of Large Language Models: Algorithmic fidelity," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-33, March.
    12. Sugat Chaturvedi & Rochana Chaturvedi, 2025. "Who Gets the Callback? Generative AI and Gender Bias," Papers 2504.21400, arXiv.org.
    13. Navid Ghaffarzadegan & Sarah Mostafavi & Hyunjung Kim, 2023. "Sociotechnical interdependencies and tipping‐point dynamics in data‐intensive services," System Dynamics Review, System Dynamics Society, vol. 39(1), pages 5-31, January.
    14. Anne Lundgaard Hansen & Seung Jung Lee, 2025. "Financial Stability Implications of Generative AI: Taming the Animal Spirits," Papers 2510.01451, arXiv.org.
    15. Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024. "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers 2406.05972, arXiv.org, revised Oct 2024.
    16. Hua Li & Qifang Wang & Ye Wu, 2025. "From Mobile Media to Generative AI: The Evolutionary Logic of Computational Social Science Across Data, Methods, and Theory," Mathematics, MDPI, vol. 13(19), pages 1-17, September.
    17. Ben Weidmann & Yixian Xu & David J. Deming, 2025. "Measuring Human Leadership Skills with Artificially Intelligent Agents," Papers 2508.02966, arXiv.org.
    18. Seung Jung Lee & Anne Lundgaard Hansen, 2025. "Financial Stability Implications of Generative AI: Taming the Animal Spirits," Finance and Economics Discussion Series 2025-090, Board of Governors of the Federal Reserve System (U.S.).
    19. Wayne Gao & Sukjin Han & Annie Liang, 2026. "How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge," Papers 2601.12343, arXiv.org.
    20. Ferraz, Vinícius & Olah, Tamas & Sazedul, Ratin & Schmidt, Robert & Schwieren, Christiane, 2025. "When Artificial Minds Negotiate: Dark Personality and the Ultimatum Game in Large Language Models," Working Papers 0768, University of Heidelberg, Department of Economics.
    21. Paola Cillo & Gaia Rubera, 2025. "Generative AI in innovation and marketing processes: A roadmap of research opportunities," Journal of the Academy of Marketing Science, Springer, vol. 53(3), pages 684-701, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:sysdyn:v:40:y:2024:i:1:n:e1761. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/0883-7066 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.