A Quantitative Game-theoretical Study on Externalities of Long-lasting Humanitarian Relief Operations in Conflict Areas

A Quantitative Game-theoretical Study on Externalities of Long-lasting Humanitarian Relief Operations in Conflict Areas

Kaiming Xiao, Haiwen Chen, Hongbin Huang, Lihua Liu, Jibing Wu

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
AI for Good. Pages 6272-6280. https://doi.org/10.24963/ijcai.2023/696

Humanitarian relief operations are often accompanied by regional conflicts around the globe, at risk of deliberate, persistent and unpredictable attacks. However, the long-term channeling of aid resources into conflict areas may influence subsequent patterns of violence and expose local communities to new risks. In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. Specifically, we first model the problem of long-lasting humanitarian relief operations in conflict areas as an online multi-stage rescuer-and-attacker interdiction game in which aid demands are revealed in an online fashion. Both models of single-source and multiple-source relief supply policy are established respectively, and two corresponding near-optimal online algorithms are proposed. In conjunction with a real case of anti-Ebola practice in conflict areas of DR Congo, we find that 1) long-lasting humanitarian relief operations aiming alleviation of crises in conflict areas can lead to indirect funding of local rebel groups; 2) the operations can activate the rebel groups to some extent, as evidenced by the scope expansion of their activities. Furthermore, the impacts of humanitarian aid intensity, frequency and supply policies on the above externalities are quantitatively analyzed, which will provide enlightening decision-making support for the implementation of related operations in the future.
Keywords:
AI for Good: Planning and Scheduling
AI for Good: Agent-based and Multi-agent Systems
AI for Good: Humans and AI
AI for Good: Uncertainty in AI