Title: Informing (or not) the Public about a Pandemic
Hüseyin Gürkan, European School of Management and Technology – Berlin
(joint work with Francis de Véricourt (ESMT Berlin) and Shouqiang Wang (University of Texas at Dallas))
Oct. 16, Friday, 1:40 p.m.
The seminar will be via Zoom.
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Facing a pandemic outbreak such as Covid-19, the government disseminates public information to a heterogeneous population and the incentives between the government and the general public may not be perfectly aligned. We characterize the government’s optimal information policy, which may not be fully transparent. An economy-biased government is fully transparent only when the healthcare consequence is sufficiently severe. Otherwise, such a government downplays the risk, and may even disclose no information. In contrast, a healthcare-biased government is fully transparent only when the healthcare consequence is sufficiently mild. Such a government otherwise exaggerates the risk, and discloses no information when the healthcare consequence is sufficiently high. It is optimal for a more balanced government to be transparent. Furthermore, as the population becomes economically more heterogeneous, the government downplays more and exaggerates less. We explicitly model and show that these results hinge on the population’s strategic behavior of avoiding the crowd.
Key words : Information Design, Bayesian Persuasion Game, Global Health, Disaster Management
Huseyin Gurkan joined ESMT Berlin as an assistant professor of Management Science in 2019. Before joining ESMT Berlin, Huseyin earned his PhD in Business Administration at the Fuqua School of Business from Duke University. He earned his Bachelor’s and Master’s degrees in Industrial Engineering both from Bilkent University. His research interests are focused on addressing operational issues in cases where system objectives (e.g., maximizing revenue, social welfare) are not aligned with incentives of self-interested, economic agents. In particular, Huseyin conducts research on designing mechanisms/rules to efficiently operate dynamic systems where companies/organizations repeatedly interact with their customers/members.