BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks

Abstract

Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in tasks such as survey response prediction and human-subject experiment simulation, there remains no systematic understanding of how well they perform across diverse behavioral science tasks. We introduce BehaviorBench, a comprehensive benchmark that evaluates foundation models along four core capabilities: (1) behavior prediction and simulation, (2) strategic decision-making, (3) subject-trait inference, and (4) behavioral knowledge application. Crucially, BehaviorBench evaluates model outputs at both the individual and distributional levels, capturing not only per-subject accuracy but also population-level alignment, an essential requirement for behavioral validity. Our evaluation shows that BehaviorBench remains challenging for leading general-purpose LLMs and behavior foundation models that are specifically trained with behavioral data. We find that individual-level and distributional performance do not always align. General-purpose LLMs tend to underestimate the diversity of human responses, whereas behavior foundation models often lag behind at individual-level prediction. Our investigation further demonstrates how fine-tuning on diverse behavioral data can improve both individual-level prediction and distributional alignment, balancing these two objectives. Our results highlight the importance of evaluation at both individual and distributional levels, establishing BehaviorBench as a foundation for developing and assessing behaviorally aligned AI systems. Our BehaviorBench and models can be accessed via https://umich-foreseer.github.io/behaviorbench/