🤖 AI Summary
This study addresses strategic monopolization and resource congestion in railway slot allocation arising from disparities in operator scale. The authors propose a novel multi-agent repeated auction mechanism integrating congestion pricing with asymmetric incentives. By incorporating congestion-sensitive base prices and scale-adjustment rules, the mechanism harmonizes efficiency and fairness under transparent governance. Leveraging an incomplete-information repeated game framework and validated through a real-time web-based multi-agent platform with human participants, this work provides the first empirical evidence—within a genuine human–agent interaction environment—that the mechanism effectively curbs strategic dominance by large operators. Results demonstrate the system’s capacity to dynamically respond to aggregate demand and activate corrective incentives; however, large operators persistently adopt high-request strategies. Qualitative analysis reveals underlying strategic motives, including maintaining market presence and imposing higher costs on competitors.
📝 Abstract
We present a multi-agent system for studying the allocation of discrete, congested resources among heterogeneous strategic agents, motivated by the problem of railway slot allocation under deregulation. Multiple operator-agents, differing in size and capacity, interact through a shared auction mechanism over repeated rounds under time-constrained decision-making. The mechanism combines a congestion-based base price that increases with aggregate demand with an asymmetric corrective adjustment that penalises the agent requesting the most slots and rewards the agent requesting the fewest, and is designed to mitigate strategic dominance by large agents while preserving transparency and congestion sensitivity. We formulate the interaction as a repeated game with incomplete information and implement the system as a real-time, web-based multi-agent environment in which human participants control individual agents and observe live marginal-cost and competitor feedback.
We report exploratory observations from two structured sessions with domain experts acting as operator-agents. The congestion mechanism responds to aggregate demand as designed and the corrective incentives are actively triggered, but agents representing large operators persist with high-request strategies despite the penalty, suggesting that corrective pricing is necessary but not sufficient to neutralise strategic dominance in this multi-agent setting. A post-session debrief indicates that participants' decisions were driven by the assumed agent role rather than personal disposition, and provides qualitative support for strategic motives, such as preserving market presence and raising rivals' costs, operating alongside short-term profit maximisation. We discuss implications for multi-agent mechanism design under asymmetric budgets and outline directions for analytical validation and larger-scale multi-agent experiments.