2000 character limit reached
Bayesian Persuasion for Containing SIS Epidemics with Asymptomatic Infection (2312.04182v2)
Published 7 Dec 2023 in eess.SY, cs.GT, and cs.SY
Abstract: 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.
- C. Nowzari, V. M. Preciado, and G. J. Pappas, “Optimal resource allocation for control of networked epidemic models,” IEEE Transactions on Control of Network Systems, vol. 4, no. 2, pp. 159–169, 2015.
- L. Zino and M. Cao, “Analysis, prediction, and control of epidemics: A survey from scalar to dynamic network models,” IEEE Circuits and Systems Magazine, vol. 21, no. 4, pp. 4–23, 2021.
- A. R. Hota, T. Sneh, and K. Gupta, “Impacts of game-theoretic activation on epidemic spread over dynamical networks,” SIAM Journal on Control and Optimization, pp. S92–S118, 2022.
- C. Eksin, J. S. Shamma, and J. S. Weitz, “Disease dynamics on a network game: A little empathy goes a long way,” Scientific Reports, vol. 7, p. 44122, 2017.
- A. Satapathi, N. K. Dhar, A. R. Hota, and V. Srivastava, “Coupled evolutionary behavioral and disease dynamics under reinfection risk,” IEEE Transactions on Control of Network Systems, In Press, 2023.
- K. Paarporn and C. Eksin, “SIS epidemics coupled with evolutionary social distancing dynamics,” in 2023 American Control Conference (ACC). IEEE, 2023, pp. 4308–4313.
- A. R. Hota, U. Maitra, E. Elokda, and S. Bolognani, “Learning to mitigate epidemic risks: A dynamic population game approach,” Dynamic Games and Applications, In Press, 2023.
- S. Y. Olmez, S. Aggarwal, J. W. Kim, E. Miehling, T. Başar, M. West, and P. G. Mehta, “Modeling presymptomatic spread in epidemics via mean-field games,” in 2022 American Control Conference (ACC). IEEE, 2022, pp. 3648–3655.
- Y. Huang and Q. Zhu, “Game-theoretic frameworks for epidemic spreading and human decision-making: A review,” Dynamic Games and Applications, vol. 12, no. 1, pp. 7–48, 2022.
- N. C. Martins, J. Certorio, and R. J. La, “Epidemic population games and evolutionary dynamics,” Automatica, vol. 153, p. 111016, 2023.
- X. Bi and C. L. Beck, “On the role of asymptomatic carriers in epidemic spread processes,” in 2021 American Control Conference (ACC). IEEE, 2021, pp. 3164–3169.
- A. Akinbi, M. Forshaw, and V. Blinkhorn, “Contact tracing apps for the COVID-19 pandemic: A systematic literature review of challenges and future directions for neo-liberal societies,” Health Information Science and Systems, vol. 9, pp. 1–15, 2021.
- D. Lewis, “Contact-tracing apps help to reduce covid infections,” Nature, vol. 591, no. 7848, pp. 19–19, 2021.
- “Exposure Notifications: Help slow the spread of COVID-19, with one step on your phone,” accessed 11-11-2023. [Online]. Available: https://www.google.com/covid19/exposurenotifications/
- E. Kamenica and M. Gentzkow, “Bayesian persuasion,” American Economic Review, vol. 101, no. 6, pp. 2590–2615, 2011.
- E. Kamenica, “Bayesian persuasion and information design,” Annual Review of Economics, vol. 11, pp. 249–272, 2019.
- D. Bergemann and S. Morris, “Information design: A unified perspective,” Journal of Economic Literature, vol. 57, no. 1, pp. 44–95, 2019.
- B. L. Ferguson, P. N. Brown, and J. R. Marden, “Avoiding unintended consequences: How incentives aid information provisioning in Bayesian congestion games,” in 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022, pp. 3781–3786.
- J. Verbree and A. Cherukuri, “Inferring the prior in routing games using public signalling,” arXiv preprint arXiv:2109.05895, 2021.
- M. Wu, S. Amin, and A. E. Ozdaglar, “Value of information in Bayesian routing games,” Operations Research, vol. 69, no. 1, pp. 148–163, 2021.
- D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar, “Informational Braess’ paradox: The effect of information on traffic congestion,” Operations Research, vol. 66, no. 4, pp. 893–917, 2018.
- J. H. Seaton, S. Hounsinou, G. Bloom, and P. N. Brown, “Competitive information provision among internet routing nodes,” in 2023 American Control Conference (ACC). IEEE, 2023, pp. 1074–1079.
- R. Aid, A. Kowli, and A. A. Kulkarni, “Signalling for electricity demand response: When is truth telling optimal?” arXiv preprint arXiv:2302.12770, 2023.
- F. Farhadi, D. Teneketzis, and S. J. Golestani, “Static and dynamic informational incentive mechanisms for security enhancement,” in 2018 European control conference (ECC). IEEE, 2018, pp. 1048–1055.
- S. Pathak and A. A. Kulkarni, “A scalable Bayesian persuasion framework for epidemic containment on heterogeneous networks,” arXiv preprint arXiv:2207.11578, 2022.
- A. Satapathi, N. K. Dhar, A. R. Hota, and V. Srivastava, “Epidemic propagation under evolutionary behavioral dynamics: Stability and bifurcation analysis,” in 2022 American Control Conference (ACC). IEEE, 2022, pp. 3662–3667.
- Ashish R. Hota (31 papers)
- Abhisek Satapathi (4 papers)
- Urmee Maitra (3 papers)