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arXiv AI By Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei

How AI Prompts Can Teach Us About the Structure of Human Behavior

Read the original on arXiv AI →

The paper presents an AI-based method that uses a large language model to emulate human decision-making by assigning it a "type vector" describing traits such as Altruism and Risk Aversion. By varying these dimensions and values, the authors fit the model to over 119,000 decisions from 78,657 participants in 10 classic economic games, finding that three dimensions—Risk Aversion, Strategic Sophistication, and Trust—sufficiently capture human behavior. The resulting type clusters, fewer than a dozen, predict behavior in new games, suggesting a low-dimensional, portable representation of human behavior across diverse settings.

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