Large Language Newsvendor: Decision Biases and Cognitive Mechanisms

• URL: http://arxiv.org/abs/2512.12552v1
• Date: Sun, 14 Dec 2025 04:51:53 GMT
• Title: Large Language Newsvendor: Decision Biases and Cognitive Mechanisms
• Authors: Jifei Liu, Zhi Chen, Yuanguang Zhong,
• Abstract summary: Large language models (LLMs) are increasingly integrated into business decision making.<n>LLMs replicate and amplify human cognitive biases.<n>This is particularly critical in high-stakes operational contexts like supply chain management.
• Score: 2.7070404673380817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Problem definition: Although large language models (LLMs) are increasingly integrated into business decision making, their potential to replicate and even amplify human cognitive biases cautions a significant, yet not well-understood, risk. This is particularly critical in high-stakes operational contexts like supply chain management. To address this, we investigate the decision-making patterns of leading LLMs using the canonical newsvendor problem in a dynamic setting, aiming to identify the nature and origins of their cognitive biases. Methodology/results: Through dynamic, multi-round experiments with GPT-4, GPT-4o, and LLaMA-8B, we tested for five established decision biases. We found that LLMs consistently replicated the classic ``Too Low/Too High'' ordering bias and significantly amplified other tendencies like demand-chasing behavior compared to human benchmarks. Our analysis uncovered a ``paradox of intelligence'': the more sophisticated GPT-4 demonstrated the greatest irrationality through overthinking, while the efficiency-optimized GPT-4o performed near-optimally. Because these biases persist even when optimal formulas are provided, we conclude they stem from architectural constraints rather than knowledge gaps. Managerial implications: First, managers should select models based on the specific task, as our results show that efficiency-optimized models can outperform more complex ones on certain optimization problems. Second, the significant amplification of bias by LLMs highlights the urgent need for robust human-in-the-loop oversight in high-stakes decisions to prevent costly errors. Third, our findings suggest that designing structured, rule-based prompts is a practical and effective strategy for managers to constrain models' heuristic tendencies and improve the reliability of AI-assisted decisions.

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