Abstract
As generative AI (Gen AI) systems take on increasingly autonomous roles in economically and socially consequential interactions, understanding their propensity to cooperate -- and the signals that shape this propensity -- has become essential. We examine cooperative behavior in frontier Gen AI models using the iterated prisoner's dilemma, manipulating counterpart reputation (positive, unknown, negative), strategy (extortion vs. generosity), and non-verbal emotional signaling (facial expressions conveying competitive or cooperative appraisals). In a first study with non-reasoning models (Claude 3.5, Gemini 2.0 Flash, GPT-4o), cooperation was systematically shaped by all three factors, paralleling patterns long documented in human behavioral research, though models varied substantially in how heavily each factor was weighted. A second study with reasoning models (Claude 4.6, Gemini 3, GPT-5.2) revealed a more concentrated reliance on strategy and reputation, a near-elimination of the Potemkin effect observed in non-reasoning models (evidenced by near-uniform cooperation in a diagnostic harmony game), and a more conditional role for emotion consistent with a hierarchical cue-weighting strategy rather than a simple loss of social sensitivity. Reasoning models also showed heterogeneous end-game behavior, ranging from sustained cooperation to systematic last-round defection effect, revealing model-specific exploitability profiles with direct practical relevance for deployment in negotiation and other multi-round interactions. Together, these findings characterize Gen AI models as increasingly sophisticated, though heterogeneous, social actors, and underscore the practical value of developing standardized cooperation benchmarks to inform the responsible deployment of Gen AI in interactive, socially consequential settings.
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Cite this article
APA 7
de Melo, C., Feng, Z., Hale, J., Terada, K., Coricelli, G., & Gratch, J. (2026). Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models. https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models
MLA 9
de Melo, Celso, et al. "Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models." https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models.
Chicago (author–date)
de Melo, Celso, Zishan Feng, James Hale, Kazunori Terada, Giorgio Coricelli, and Jonathan Gratch. 2026. "Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models." https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models.
Harvard
de Melo, C., Feng, Z., Hale, J., Terada, K., Coricelli, G. and Gratch, J. (2026) 'Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models', Available at: https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models.
Vancouver
de Melo C, Feng Z, Hale J, Terada K, Coricelli G, Gratch J. Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models. https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models
IEEE
C. de Melo, Z. Feng, J. Hale, K. Terada, G. Coricelli, and J. Gratch, "Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models," https://omanscience.com/en/articles/reputation-strategy-and-emotion-effects-on-generative-ai-cooperation-a-comparison-across-reasoning-and-non-reasoning-models.