More at Stake: How Payoff and Language Shape LLM Agent Strategies in Cooperation Dilemmas

• URL: http://arxiv.org/abs/2601.19082v1
• Date: Tue, 27 Jan 2026 01:36:50 GMT
• Title: More at Stake: How Payoff and Language Shape LLM Agent Strategies in Cooperation Dilemmas
• Authors: Trung-Kiet Huynh, Dao-Sy Duy-Minh, Thanh-Bang Cao, Phong-Hao Le, Hong-Dan Nguyen, Nguyen Lam Phu Quy, Minh-Luan Nguyen-Vo, Hong-Phat Pham, Pham Phu Hoa, Thien-Kim Than, Chi-Nguyen Tran, Huy Tran, Gia-Thoai Tran-Le, Alessio Buscemi, Le Hong Trang, The Anh Han,
• Abstract summary: LLMs increasingly act as autonomous agents in interactive and multi-agent settings.<n>We investigate how payoff magnitude and linguistic context shape LLM strategies in repeated social dilemmas.<n>Across models and languages, we observe consistent behavioral patterns, including incentive-sensitive conditional strategies and cross-linguistic divergence.
• Score: 1.6487772637295166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLMs increasingly act as autonomous agents in interactive and multi-agent settings, understanding their strategic behavior is critical for safety, coordination, and AI-driven social and economic systems. We investigate how payoff magnitude and linguistic context shape LLM strategies in repeated social dilemmas, using a payoff-scaled Prisoner's Dilemma to isolate sensitivity to incentive strength. Across models and languages, we observe consistent behavioral patterns, including incentive-sensitive conditional strategies and cross-linguistic divergence. To interpret these dynamics, we train supervised classifiers on canonical repeated-game strategies and apply them to LLM decisions, revealing systematic, model- and language-dependent behavioral intentions, with linguistic framing sometimes matching or exceeding architectural effects. Our results provide a unified framework for auditing LLMs as strategic agents and highlight cooperation biases with direct implications for AI governance and multi-agent system design.

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