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

2026-08-18

AI Summary

研究通过给AI分配类型向量并让其在不同场景中做选择，发现人类行为可以用风险规避、策略复杂度和信任三个维度来近似表示。

Abstract

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 prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) becomes ``You are a player characterized by the following profile: 2 out of 5 in Altruism, 4 out of 5 in Risk Aversion,'' after which it is prompted to make choices. We vary the dimensions (e.g., Altruism, Fairness, Trust, $\dots$) and values (e.g., 1--5) to minimize distance to human choices. Applying the method to 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles, we find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. Moreover, the types needed to fit individuals across games cluster into fewer than a dozen groups, and can predict behavior in held-out games with different rules and available actions. The results suggest that behavior across diverse settings can be approximated by a low-dimensional, portable representation, supporting the possibility of general yet parsimonious theories across the behavioral sciences. More broadly, the method can provide insights into the structure of many human behaviors.

Problem

• Human Behavior
• Structure and Complexity
• AI-based Method
• Economic Games
• Behavioral Dimensions

Innovation

• AI-based method
• type vector
• behavioral structure
• low-dimensional representation
• behavior prediction