Using Language Models to Decipher the Motivation Behind Human Behaviors

Yutong Xie, Qiaozhu Mei, Walter Yuan, Matthew O. Jackson

Abstract: 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 induce people to think about. We discuss how this provides a first step towards a non-standard 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.

External IDs:dblp:journals/corr/abs-2503-15752