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Integrating Machine Behavior into Human Subject Experiments: A User-Friendly Toolkit and Illustrations

Author

Listed:
  • Christoph Engel

    (Max Planck Institute for Research on Collective Goods, Bonn & University of Bonn)

  • Max R. P. Grossmann

    (University of Cologne)

  • Axel Ockenfels

    (University of Cologne & Max Planck Institute for Research on Collective Goods, Bonn)

Abstract

Large Language Models (LLMs) have the potential to profoundly transform and enrich experimental economic research. We propose a new software framework, “alter_ego”, which makes it easy to design experiments between LLMs and to integrate LLMs into oTreebased experiments with human subjects. Our toolkit is freely available at github.com/mrpg/ego. To illustrate, we run differently framed prisoner’s dilemmas with interacting machines as well as with humanmachine interaction. Framing effects in machine-only treatments are strong and similar to those expected from previous human-only experiments, yet less pronounced and qualitatively different if machines interact with human participants.

Suggested Citation

  • Christoph Engel & Max R. P. Grossmann & Axel Ockenfels, 2024. "Integrating Machine Behavior into Human Subject Experiments: A User-Friendly Toolkit and Illustrations," ECONtribute Discussion Papers Series 302, University of Bonn and University of Cologne, Germany.
    • Handle: RePEc:ajk:ajkdps:302
    as

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    File URL: https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_302_2024.pdf
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    References listed on IDEAS

    as
    1. Dufwenberg, Martin & Gächter, Simon & Hennig-Schmidt, Heike, 2011. "The framing of games and the psychology of play," Games and Economic Behavior, Elsevier, vol. 73(2), pages 459-478.
    2. Fischbacher, Urs & Gachter, Simon & Fehr, Ernst, 2001. "Are people conditionally cooperative? Evidence from a public goods experiment," Economics Letters, Elsevier, vol. 71(3), pages 397-404, June.
    3. Anna Dreber & Tore Ellingsen & Magnus Johannesson & David Rand, 2013. "Do people care about social context? Framing effects in dictator games," Experimental Economics, Springer;Economic Science Association, vol. 16(3), pages 349-371, September.
    4. Friederike Mengel, 2018. "Risk and Temptation: A Meta‐study on Prisoner's Dilemma Games," Economic Journal, Royal Economic Society, vol. 128(616), pages 3182-3209, December.
    5. Tim Johnson & Nick Obradovich, 2022. "Measuring an artificial intelligence agent's trust in humans using machine incentives," Papers 2212.13371, arXiv.org.
    6. John J. Horton, 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.
    7. Smith, Vernon L, 1976. "Experimental Economics: Induced Value Theory," American Economic Review, American Economic Association, vol. 66(2), pages 274-279, May.
    8. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    9. Steve Phelps & Yvan I. Russell, 2023. "Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics," Papers 2305.07970, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Software for experiments; large language models; humanmachine interaction; framing;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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