Escaping the Nash Trap: Structural Estimation and Alignment of Strategic Reasoning in Large Language Models

This paper investigates a critical strategic mismatch between Large Language Models (LLMs) and human decision-makers in competitive environments. Through game-theoretic experiments, the researchers demonstrate that LLMs predominantly act as Nash-type reasoners, assuming their opponents are perfectly rational, whereas humans exhibit bounded rationality and varied reasoning depths. This overestimation of human sophistication often leads LLMs into a Nash trap, where equilibrium play fails to maximize payoffs against actual human behavior. To rectify this, the authors propose supervised fine-tuning methods, including Trap-Aware SFT, which calibrates model responses to empirical human benchmarks. Their findings suggest that effective human–AI alignment requires models to possess not just high reasoning capabilities, but also calibrated expectations of human behavior. Ultimately, the study advocates for a selective deployment architecture that preserves equilibrium play while adapting strategies when human interaction makes it more profitable.