Using large language models to categorize strategic situations and decipher motivations behind human behaviors - Yutong Xie; Qiaozhu Mei; Walter Yuan; Matthew O. Jackson (Proceedings of the National Academy of Sciences, 2025)
By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios might induce people to think about. We discuss how this provides a step toward a nonstandard method of inferring (deciphering) the motivations behind the human behaviors. We also show how this deciphering process can be used to categorize differences in the behavioral tendencies of different populations.