Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
arXiv:2604.17220v2 Announce Type: replace-cross Abstract: Modeling coordination among generative agents in complex multi-round decision-making presents a core challenge for AI and operations management. Although behavioral experiments have revealed cognitive biases behind supply chain inefficiencies, traditional methods face scalability and control limitations. We introduce a scalable experimental paradigm using Large Language Models (LLMs) to simulate multi-stage supply chain dynamics. Grounded in a Hierarchical Reasoning Framework, this study specifically analyzes the impact of cognitive het
The rapid advancement and widespread adoption of Large Language Models (LLMs) enable new methodologies for complex simulation studies that were previously constrained by scalability and control.
This development offers a scalable and controlled way to understand and mitigate behavioral biases in critical systems like supply chains, ultimately improving their efficiency and resilience.
Traditional behavioral experiments facing scalability issues can now be augmented or replaced by LLM-based simulations, providing deeper insights into complex multi-agent decision-making dynamics.
- · Operations Management Researchers
- · Supply Chain Optimization Platforms
- · AI/ML Model Developers
- · Logistics Sector
- · Traditional Econometric Modeling
- · Small-scale Behavioral Labs
- · Inefficient Supply Chains
Companies will gain better tools to identify and correct inefficiencies caused by human cognitive biases in their operational processes.
The improved understanding of economic behaviors through LLM simulations could lead to more robust and AI-driven policy recommendations for economic stability.
This simulation paradigm might expand beyond supply chains to model societal dynamics, influencing urban planning, public policy, and even geopolitical strategy.
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Read at arXiv cs.AI