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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.04182 |
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| _version_ | 1866929485455032320 |
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| author | Hota, Ashish R. Satapathi, Abhisek Maitra, Urmee |
| author_facet | Hota, Ashish R. Satapathi, Abhisek Maitra, Urmee |
| contents | We investigate the strategic behavior of a large population of agents who decide whether to adopt a costly partially effective protection or remain unprotected against the susceptible-infected-susceptible epidemic. In contrast with most prior works on epidemic games, we assume that the agents are not aware of their true infection status while making decisions. We adopt the Bayesian persuasion framework where the agents receive a noisy signal regarding their true infection status, and maximize their expected utility computed using the posterior probability of being infected conditioned on the received signal. We characterize the stationary Nash equilibrium of this setting under suitable assumptions, and identify conditions under which partial information disclosure leads to a smaller proportion of infected individuals at the equilibrium compared to full information disclosure, and vice versa. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04182 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Persuasion for Containing SIS Epidemics with Asymptomatic Infection Hota, Ashish R. Satapathi, Abhisek Maitra, Urmee Systems and Control Computer Science and Game Theory We investigate the strategic behavior of a large population of agents who decide whether to adopt a costly partially effective protection or remain unprotected against the susceptible-infected-susceptible epidemic. In contrast with most prior works on epidemic games, we assume that the agents are not aware of their true infection status while making decisions. We adopt the Bayesian persuasion framework where the agents receive a noisy signal regarding their true infection status, and maximize their expected utility computed using the posterior probability of being infected conditioned on the received signal. We characterize the stationary Nash equilibrium of this setting under suitable assumptions, and identify conditions under which partial information disclosure leads to a smaller proportion of infected individuals at the equilibrium compared to full information disclosure, and vice versa. |
| title | Bayesian Persuasion for Containing SIS Epidemics with Asymptomatic Infection |
| topic | Systems and Control Computer Science and Game Theory |
| url | https://arxiv.org/abs/2312.04182 |