English

Post-Disaster Resource Redistribution and Cooperation Evolution Based on Two-Layer Network Evolutionary Games

Physics and Society 2026-01-30 v1 Computer Science and Game Theory

Abstract

In the aftermath of large-scale disasters, the scarcity of resources and the paralysis of infrastructure raise severe challenges to effective post-disaster recovery. Efficient coordination between shelters and victims plays a crucial role in building community resilience, yet the evolution of two-layer behavioral feedback between these two groups through network coupling remains insufficiently understood. Here, this study develops a two-layer network to capture the cross-layer coupling between shelters and victims. The upper layer uses a post-disaster emergency resource redistribution model within the framework of the public goods game, while the lower layer adopts a cooperative evolutionary game to describe internal victim interactions. Monte Carlo simulations on scale-free networks reveal threshold effects of incentives: moderate public goods enhancement and subsidies promote cooperation, whereas excessive incentives induce free-riding. In contrast, credible and well-executed punishment effectively suppresses defection. Targeted punishment of highly connected shelters significantly enhances cooperation under resource constraints. A comparative analysis using a network generated from the actual coordinates of Beijing shelters confirms the model's generality and practical applicability. The findings highlight the importance of calibrated incentives, enforceable sanctions, and structural targeting in fostering robust cooperation across organizational and individual levels in post-disaster environments.

Keywords

Cite

@article{arxiv.2601.22021,
  title  = {Post-Disaster Resource Redistribution and Cooperation Evolution Based on Two-Layer Network Evolutionary Games},
  author = {Yu Chen and Genjiu Xu and Sinan Feng and Chaoqian Wang},
  journal= {arXiv preprint arXiv:2601.22021},
  year   = {2026}
}

Comments

11 pages, 14 figures, accepted for publication in Chaos

R2 v1 2026-07-01T09:26:11.723Z