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Playing games with GPT: What can we learn about a large language model from canonical strategic games?

Author

Listed:
  • Philip Brookins

    (University of South Carolina)

  • Jason DeBacker

    (University of South Carolina)

Abstract

We aim to understand fundamental preferences over fairness and cooperation embedded in artificial intelligence (AI). We do this by having a large language model (LLM), GPT-3.5, play two classic games: the dictator game and the prisoner's dilemma game. We compare the decisions of the LLM to those of humans in laboratory experiments. We find that the LLM replicates human tendencies towards fairness and cooperation. It does not choose the optimal strategy in most cases. Rather, it shows a tendency towards fairness in the dictator game, even more so than human participants. In the prisoner's dilemma, the LLM displays rates of cooperation much higher than human participants (about 65% versus 37% for humans). These findings aid our understanding of the ethics and rationality embedded in AI.

Suggested Citation

  • Philip Brookins & Jason DeBacker, 2024. "Playing games with GPT: What can we learn about a large language model from canonical strategic games?," Economics Bulletin, AccessEcon, vol. 44(1), pages 25-37.
    • Handle: RePEc:ebl:ecbull:eb-23-00457
    as

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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Large language models (LLMs); Generative Pre-trained Transformer (GPT); Experimental Economics; Game Theory; AI;
    All these keywords.

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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