Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory

• URL: http://arxiv.org/abs/2509.04847v1
• Date: Fri, 05 Sep 2025 06:55:15 GMT
• Title: Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
• Authors: Mukul Singh, Arjun Radhakrishna, Sumit Gulwani,
• Abstract summary: We study the dynamics of language model behavior in the iterated prisoner's dilemma (IPD)<n>We find that language models achieve performance on par with, and in some cases exceeding, the best-known classical strategies.<n> Behavioral analysis reveals that language models exhibit key properties associated with strong cooperative strategies.
• Score: 11.715338068067373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models are increasingly deployed in interactive online environments, from personal chat assistants to domain-specific agents, raising questions about their cooperative and competitive behavior in multi-party settings. While prior work has examined language model decision-making in isolated or short-term game-theoretic contexts, these studies often neglect long-horizon interactions, human-model collaboration, and the evolution of behavioral patterns over time. In this paper, we investigate the dynamics of language model behavior in the iterated prisoner's dilemma (IPD), a classical framework for studying cooperation and conflict. We pit model-based agents against a suite of 240 well-established classical strategies in an Axelrod-style tournament and find that language models achieve performance on par with, and in some cases exceeding, the best-known classical strategies. Behavioral analysis reveals that language models exhibit key properties associated with strong cooperative strategies - niceness, provocability, and generosity while also demonstrating rapid adaptability to changes in opponent strategy mid-game. In controlled "strategy switch" experiments, language models detect and respond to shifts within only a few rounds, rivaling or surpassing human adaptability. These results provide the first systematic characterization of long-term cooperative behaviors in language model agents, offering a foundation for future research into their role in more complex, mixed human-AI social environments.

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