Decision and Gender Biases in Large Language Models: A Behavioral Economic Perspective
This addresses the problem of bias in LLMs for users in economic and organizational contexts, but it is incremental as it builds on existing behavioral economics frameworks.
The study investigated whether advanced large language models (LLMs) behave as rational agents or reproduce human behavioral biases in decision-making, finding that models like Google Gemma7B and Qwen showed attenuated but persistent deviations from rationality, including moderate fairness concerns and mild loss aversion.
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.