6 papers
How AI Prompts Can Teach Us About the Structure of Human Behavior
Matthew O. Jackson, Benjamin S. Manning, Yutong Xie +2
We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a ``type vector'' and then p…
BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks
Jin Huang, Yutong Xie, Wanli Song +4
Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in individual tasks suc…
AI Behavioral Science
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16
We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it be…
Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors
Yutong Xie, Qiaozhu Mei, Walter Yuan +1
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 prom…
Be.FM: Open Foundation Models for Human Behavior
Yutong Xie, Zhuoheng Li, Xiyuan Wang +10
Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of th…
How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
Yutong Xie, Yiyao Liu, Zhuang Ma +5
The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns. As a supplemen…