Collaborate or compete? Decentralized resource allocation between local authorities during COVID-19 based on evolutionary game theory

Abstract

Facing the threaten of world-wide pandemics such as COVID-19, critical resources including masks, vaccines, and medical equipment play an important role in infection risk mitigation and outbreak containment. However, with limited market resources, as it is hard to satisfy all the demands, a natural question is that how to make the allocation? Given the complicated and varying situation, this paper models the interaction between two local authorities making resource allocation decisions within the background of epidemic following the methodology of evolutionary game theory. The proposed model can be applied to practical resource allocation determinations on examining how critical factors influence allocation outcomes and offering strategic insights on whether local authorities should adopt collaborative or competitive approaches under varying conditions. A case study along with the sensitivity analysis based on the real-world background is further constructed to prove the feasibility and efficiency of this model. The results indicate that while collaboration is generally a better strategy in most situations, competition may emerge when potential benefits outweigh cooperation incentives—particularly depending on pandemic severity and transmission rates. Interestingly, both excessively high and relatively low severity levels, as well as external impacts, can trigger competitive behavior. The novelty of this study lies in providing a strategic tool that helps solve a practical resource allocation problem considering the dynamic characteristic of COVID-19. This paper illustrates managerial insights on reminding the decision-makers that extra efforts may need to promote a demand-driven resource allocation (the one with higher demand gets more resources) in the fast-spread and emergency stage of a pandemic.