Dynamic Feedback Regulation in Multi-Agent Emergency Supply Stockpiling: An Evolutionary Game and System Stability Perspective

In the context of increasingly frequent and highly unpredictable unconventional emergencies and growing supply chain uncertainty, traditional static reward-and-punishment mechanisms often fail to curb enterprises’ speculative stockpiling, leading to strategic oscillations and instability in collaborative emergency supply stockpiling systems. To address the lack of attention to dynamic governance mechanisms, this paper develops a tripartite evolutionary game model involving the government, stockpiling enterprises, and the public under the assumption of bounded rationality. The model examines how a dynamic reward-and-punishment mechanism affects the evolution of collaborative stockpiling strategies and system stability. Results show that under a static mechanism, enterprise strategies are highly sensitive to fluctuations in speculative returns and regulatory costs, making stable equilibrium difficult to achieve. In contrast, a behavioral-state-dependent dynamic mechanism adjusts reward-and-punishment intensities in response to feedback on enterprise behavior, uses changes in the proportion of enterprises choosing responsible stockpiling as the trigger for adaptive adjustment, reshapes enterprises’ payoff structures, and thereby forms an adaptive governance mechanism based on behavioral feedback. This suppresses speculative stockpiling and promotes convergence toward stability. The analysis indicates that increasing reward-and-punishment intensity does not necessarily improve governance effectiveness: excessive penalties may increase volatility, whereas an appropriate range of reward-and-punishment intensities improves system stability and governance efficiency. Public oversight functions primarily as a phased external constraint; as responsible stockpiling behavior gradually stabilizes, the system’s dependence on sustained high-intensity oversight gradually decreases. These findings provide a decision-support framework for policymakers to translate evolutionary game analysis into adaptive administrative regulation.