Large language models instantiate evolutionarily robust strategies of cooperation

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ID: 316955
2026
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Abstract
Abstract Large language models (LLMs) are increasingly deployed to support human decision-making. This use of LLMs has concerning implications, especially when their prescriptions affect the welfare of others. To gauge how LLMs make social decisions, we explore whether five leading models produce sensible strategies in the repeated prisoner's dilemma, which is the main metaphor of reciprocal cooperation. First, we measure the propensity of LLMs to cooperate in a neutral setting, without using language reminiscent of how this game is usually presented. We record to what extent LLMs implement Nash equilibria or other well-known strategy classes. Thereafter, we explore how LLMs adapt their strategies to changes in parameter values. We vary the game's stopping probability, the payoff values, and if the total number of rounds is commonly known. We also study the effect of different framings. In each case, we test whether the adaptations of the LLMs are in line with basic intuition, theoretical predictions of evolutionary game theory, and experimental evidence of human participants. While all LLMs perform well in many of the tasks, none of them exhibit full consistency over all tasks. We also conduct tournaments between the inferred LLM strategies and study direct interaction between LLMs in games over ten rounds with known or unknown last round. Our experiments shed light on how current LLMs instantiate reciprocal cooperation.
Reference Key
openalex_W7164418399 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Saptarshi Pal, Abhishek Mallela, Lenz Pracher, Chiyu Wei, Feng Fu, Santiago Schnell, Martin A Nowak
Journal PNAS nexus
Year 2026
DOI
10.1093/pnasnexus/pgag210
URL
https://doi.org/10.1093/pnasnexus/pgag210
Keywords Keywords not found

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