Large Language Models Predict Human Social Behavior via Interpretable Mechanisms
This study introduces MindEvolve, an autonomous workflow in which multiple large language models generate interpretable symbolic models of cognition for a battery of socioeconomic games spanning four core domains of social cognition—economic preferences, social preferences, social reasoning (theory of mind), and recursive planning—with expert human evaluators assessing interpretability and theoretical coherence; results show that most LLMs robustly capture economic and social preferences in relatively simple strategic settings and in some cases generate novel models integrating broader knowledge than those proposed by human experts, while their capacity to model more complex psychological processes remains limited, though a subset of state-of-the-art models shows promising performance on h