Decision and Gender Biases in Large Language Models: A Behavioral Economic Perspective

TL;DR

This paper examines whether large language models exhibit human-like biases in decision-making, revealing they show moderate fairness concerns, mild loss aversion, and gender-conditioned differences, which could impact their use in economic and organizational contexts.

Contribution

It provides the first behavioral economic analysis of advanced LLMs, revealing their decision biases and gender-conditioned behaviors compared to human benchmarks.

Findings

Models show moderate fairness concerns.

Models exhibit mild loss aversion.

Gender-conditioned differences are subtle but present.

Abstract

Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rational agents or whether they reproduce human behavioural tendencies when faced with classic decision problems. Using two canonical experiments in behavioural economics, the ultimatum game and a gambling game, we elicit decisions from two state of the art models, Google Gemma7B and Qwen, under neutral and gender conditioned prompts. We estimate parameters of inequity aversion and loss-aversion and compare them with human benchmarks. The models display attenuated but persistent deviations from rationality, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences.