Abstract
Many epidemic networks admit the partition of the population into three compartments of respective susceptible, infected, and removed individuals. These epidemics involve a conflict between the agent who is propagating the threat and the defender who tries to limit the importance of the propagation. In case of incapability of both agents to monitor the transitions of the network state, this conflict is generally modeled by a stochastic game. The resolution of this class of game is general enough, but remains unscalable. To overcome the curse of dimensionality, we propose a new framework that takes into account the network topology, and we show that the best strategy for each player at each period to optimize his/her overall outcome is to focus on the set of most influential nodes. That is, the use of players’ memory is no longer necessary and, as a matter of result, the proposed algorithm is less time and memory consumptive than the value iteration-based algorithms.
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Acknowledgments
Research was sponsored by the U.S. Army Research Office and was accomplished under Cooperative Agreement Numbers W911NF-19-2-0150, W911NF-22-2-0175 and Grant Number W911NF-21-1-0326. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Tsemogne, O., Kouam, W., Anwar, A.H., Hayel, Y., Kamhoua, C., Deugoué, G. (2023). A Network Centrality Game for Epidemic Control. In: Fang, F., Xu, H., Hayel, Y. (eds) Decision and Game Theory for Security. GameSec 2022. Lecture Notes in Computer Science, vol 13727. Springer, Cham. https://doi.org/10.1007/978-3-031-26369-9_13
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DOI: https://doi.org/10.1007/978-3-031-26369-9_13
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