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Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing

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  • Nunzio Lor`e
  • Babak Heydari

Abstract

This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or defect for individual gain. Crucially, we extend our analysis to examine the role of contextual framing, such as diplomatic relations or casual friendships, in shaping the models' decisions. Our findings reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual framing, it shows limited ability to engage in abstract strategic reasoning. Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and context, but LLaMa-2 exhibits a more nuanced understanding of the games' underlying mechanics. These results highlight the current limitations and varied proficiencies of LLMs in strategic decision-making, cautioning against their unqualified use in tasks requiring complex strategic reasoning.

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  • Nunzio Lor`e & Babak Heydari, 2023. "Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing," Papers 2309.05898, arXiv.org.
    • Handle: RePEc:arx:papers:2309.05898
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    References listed on IDEAS

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    1. Yiting Chen & Tracy Xiao Liu & You Shan & Songfa Zhong, 2023. "The emergence of economic rationality of GPT," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(51), pages 2316205120-, December.
    2. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    3. Steve Phelps & Yvan I. Russell, 2023. "Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics," Papers 2305.07970, arXiv.org.
    4. Joseph N. Luchman, 2021. "Determining relative importance in Stata using dominance analysis: domin and domme," Stata Journal, StataCorp LP, vol. 21(2), pages 510-538, June.
    5. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
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