AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
This work addresses concerns about the reliability of LLM agents in real-world business operations, specifically inventory management, by providing a benchmark to assess biases, though it is incremental in focusing on evaluation rather than novel solutions.
The paper tackles the problem of evaluating decision-making biases in LLM agents used as inventory managers in uncertain supply chain contexts, revealing that different LLMs exhibit varying degrees of bias similar to humans and exploring mitigation strategies like cognitive reflection and information sharing.
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.