A Dynamic Population Model of Strategic Interaction and Migration under Epidemic Risk
| Autor: | Ezzat Elokda, Saverio Bolognani, Ashish R. Hota |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Optimization and Control (math.OC) Computer Science - Computer Science and Game Theory FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control Computer Science and Game Theory (cs.GT) |
| Zdroj: | 2021 60th IEEE Conference on Decision and Control (CDC) |
| DOI: | 10.3929/ethz-b-000549136 |
| Popis: | In this paper, we show how a dynamic population game can model the strategic interaction and migration decisions made by a large population of agents in response to epidemic prevalence. Specifically, we consider a modified susceptible-asymptomatic-infected-recovered (SAIR) epidemic model over multiple zones. Agents choose whether to activate (i.e., interact with others), how many other agents to interact with, and which zone to move to in a time-scale which is comparable with the epidemic evolution. We define and analyze the notion of equilibrium in this game, and investigate the transient behavior of the epidemic spread in a range of numerical case studies, providing insights on the effects of the agents' degree of future awareness, strategic migration decisions, as well as different levels of lockdown and other interventions. One of our key findings is that the strategic behavior of agents plays an important role in the progression of the epidemic and can be exploited in order to design suitable epidemic control measures. 2021 60th IEEE Conference on Decision and Control (CDC) ISBN:978-1-6654-3659-5 ISBN:978-1-6654-3658-8 ISBN:978-1-6654-3660-1 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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