AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager

• URL: http://arxiv.org/abs/2508.11416v1
• Date: Fri, 15 Aug 2025 11:38:19 GMT
• Title: AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
• Authors: Xuhua Zhao, Yuxuan Xie, Caihua Chen, Yuxiang Sun,
• Abstract summary: AIM-Bench is a novel benchmark designed to assess the decision-making behaviour of large language models (LLMs) in uncertain supply chain management scenarios.<n>Our results reveal that different LLMs typically exhibit varying degrees of decision bias that are similar to those observed in human beings.
• Score: 9.21215885702746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in mathematical reasoning and the long-term planning capabilities of large language models (LLMs) have precipitated the development of agents, which are being increasingly leveraged in business operations processes. Decision models to optimize inventory levels are one of the core elements of operations management. However, the capabilities of the LLM agent in making inventory decisions in uncertain contexts, as well as the decision-making biases (e.g. framing effect, etc.) of the agent, remain largely unexplored. This prompts concerns regarding the capacity of LLM agents to effectively address real-world problems, as well as the potential implications of biases that may be present. To address this gap, we introduce AIM-Bench, a novel benchmark designed to assess the decision-making behaviour of LLM agents in uncertain supply chain management scenarios through a diverse series of inventory replenishment experiments. Our results reveal that different LLMs typically exhibit varying degrees of decision bias that are similar to those observed in human beings. In addition, we explored strategies to mitigate the pull-to-centre effect and the bullwhip effect, namely cognitive reflection and implementation of information sharing. These findings underscore the need for careful consideration of the potential biases in deploying LLMs in Inventory decision-making scenarios. We hope that these insights will pave the way for mitigating human decision bias and developing human-centred decision support systems for supply chains.

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