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

Modeling the Structure of Human Behavior with AI Prompt Vectors

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arXiv AI
Aug 20

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

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.

By Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei
arXiv AI
Sep 18

Modeling Human Behavior with Type Vectors Using AI

The paper presents an AI-based method that models human behavior by assigning a language model a vector of trait intensities—called a type vector—and asking it to predict actions in various settings. By adjusting traits such as Altruism, Risk Aversion, Fairness, and Trust, the authors fit the model to 119,147 decisions from 78,657 subjects across 35 countries and 10 economic games, finding that three dimensions (Risk Aversion, Strategic Sophistication, and Trust) closely match human choices. The resulting type vectors cluster into fewer than a dozen groups and can predict behavior in new games with different rules, demonstrating the method’s generalizability and interpretability.

By Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei
arXiv AI
6d ago

Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models

The paper introduces the Preference-Behavior Model (PBM), which jointly infers Large Language Model (LLM) preferences and imperfect decision-making mechanisms from observed choices. By applying PBM to three social dilemma games—Public Goods, Collective-Risk, and Common-Pool Resource—the authors show that preferences derived from one dilemma can predict behavior in others, whereas traditional behavioral economic elicitation instruments fail to do so consistently. The study also evaluates how well inferred preferences align with persona instructions, revealing both broad agreement and specific divergences across dimensions, LLMs, and games.

By Axel Abels, Elias Fern\'andez Domingos, Tom Lenaerts