LLMs just bypassed the hardest part of behavioral economics: expensive surveys

For decades, modeling how real people make decisions, especially under risk and uncertainty, hit a hard practical wall. Cumulative prospect theory (CPT) captures the biases classical economics ignores: loss aversion, reference dependence, the tendency to overweight tiny probabilities. But to use CPT at scale, researchers need individual-level parameters that are stubbornly expensive to collect. Surveys are too small, controlled experiments too homogeneous, and the diversity of human decision-making stays out of reach.

A team from Tianjin University and the University of Nottingham Ningbo China has published what may be the first systematic demonstration that large language models can sidestep that bottleneck entirely. In a preprint titled "Reproducing human biases in route choice using large language models," the researchers show that LLM-based agents, 3,000 of them, each with a distinct demographic and psychological profile, produce choice patterns that map onto CPT's predictions with a goodness-of-fit above 0.