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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0, Open
Journal article
A game-theoretic approach for hierarchical epidemic control
Autonomous agents and multi-agent systems, v 39(14), 14
27 Feb 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
We design and analyze a multi-level game-theoretic model of hierarchical policy interventions for epidemic control, such as those in response to the COVID-19 pandemic. Our model captures the potentially mismatched priorities among a hierarchy of policy-makers (e.g., federal, state, and local governments) with respect to two cost components that have opposite dependence on the policy strength—post-intervention infection rates and the socio-economic cost of policy implementation. Additionally, our model includes a crucial third factor in decisions: a cost of non-compliance with the policy-maker immediately above in the hierarchy, such as non-compliance of counties with state-level policies. We propose two novel algorithms for approximating solutions to such games. The first is based on best response dynamics (BRD) and exploits the tree structure of the game. The second combines quadratic integer programming (QIP), which enables us to collapse the two lowest levels of the game, with the best response dynamics. We experimentally characterize the scalability and equilibrium approximation quality of our two approaches against model parameters. Finally, we conduct experiments in simulations based on both synthetic and real-world data under various parameter configurations and analyze the resulting (approximate) equilibria to gain insight into the impact of decentralization on overall welfare (measured as the negative sum of costs) as well as emergent properties like social welfare, free-riding, and fairness in cost distribution among policy-makers.
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Details
- Title
- A game-theoretic approach for hierarchical epidemic control
- Creators
- Feiran Jia - Pennsylvania State UniversityAditya Mate - Harvard UniversityZun Li - University of MichiganShahin Jabbari (Corresponding Author) - Drexel University, Computer ScienceMithun Chakraborty - University of MichiganMilind Tambe - Harvard UniversityMichael Wellman - University of MichiganYevgeniy Vorobeychik - Washington University in St. Louis
- Publication Details
- Autonomous agents and multi-agent systems, v 39(14), 14
- Publisher
- Springer Nature
- Number of pages
- 37
- Grant note
- Army Research Office
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001434423800001
- Scopus ID
- 2-s2.0-85219645361
- Other Identifier
- 991022030472104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence