How generative AI behaves in the newsvendor problem: a behavioral experimental study

Abstract

This study investigates behavioral biases of generative artificial intelligence (AI) models, specifically GPT-4o and Claude-Haiku-4.5, in inventory management using the newsvendor problem. This study compares AI decision-making with human-subject experiments to assess whether large language models (LLMs) replicate human cognitive bias and to identify prompt-design strategies that improve alignment with optimal outcomes. Controlled newsvendor experiments were conducted with generative AI models, mirroring established human-subject laboratory protocols. Prompt framing was systematically varied across three modifications: removing explicit waste and missed-profit information, simplifying instruction format and providing explicit optimization formulas. Results were benchmarked against normative economic predictions and existing human behavioral findings. Generative AI exhibits human-like human biases including risk aversion, loss aversion and demand chasing, but exhibits a stronger demand-chasing tendency than human participants. It responds to hypothetical incentives and displays bounded rationality. Prompt design significantly influences decision quality, producing decisions closer to theoretical benchmarks. This study empirically tests generative AI behavioral biases within a structured operations management experiment. It introduces a replicable methodology, extends findings across two architecturally distinct LLMs from different developers, and demonstrates that deliberate prompt design meaningfully reduces AI decision bias. The study also contributes a conceptual distinction between functionally analogous behavioral patterns and intrinsic psychological dispositions in LLMs, offering a more precise interpretive framework for AI decision-making research in operational contexts.