The Behavioral Economics of Artificial Intelligence: Understanding and Mitigating Biases in Large Language Models

Abstract: As large language models (LLMs) become integral to economic and financial decision-making, understanding their systematic behavioral patterns is critical for organizations and policymakers. This article synthesizes emerging research on the "behavioral economics of AI," examining how leading LLM families exhibit distinct biases in preference-based versus belief-based tasks. Drawing on cognitive psychology frameworks and experimental economics methodologies, we analyze patterns showing that advanced LLMs increasingly mirror human-like irrationality in preference tasks while demonstrating enhanced rationality in belief formation. We explore organizational implications across sectors including financial services, healthcare, and public administration, presenting evidence-based strategies for bias mitigation. The article concludes with frameworks for building organizational capabilities to evaluate, monitor, and govern LLM deployment in decision-critical environments, emphasizing the importance of understanding AI as a novel class of economic agent with distinct behavioral characteristics requiring systematic oversight.

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