Evaluating the ability of large Language models to predict human social decisions.

ABSTRACT: Recent advances in large language models (LLMs) have highlighted their potential to predict human decisions. In two studies, we compared predictions by GPT-3.5 and GPT-4 across 51 scenarios (9,600 responses) against published data from 2,104 human participants within an evolutionary-psychology framework. We further examined our findings with GPT-4o across eight social-group and kinship conditions (1,600 responses). Our results revealed behavioral differences between humans and LLMs' predictions: Humans showed a greater sensitivity to kinship and group size than the LLMs when making life-death decisions. LLMs align closer with humans with a higher risk-seeking preference in financial domains. While human choices followed Prospect theory's value function (risk-averse in gains, risk-seeking i

SUBMITTER: Xiao F 

PROVIDER: S-EPMC12405550 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature