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Optimal dynamic confinement under uncertainty and learning

In a new paper, network member Christian Gollier analyzes the uncertainty surrounding how contagious a virus is and how that affects optimal suppression.

Many have emphasized that uncertainty and learning should lead to tougher early suppression of a virus to give time to learn. In this paper, Christian Gollier shows that this is not necessarily true if the uncertainty is about how contagious the virus is -- this kind of uncertainty calls for less suppression in the early stage. The intuition is somewhat elusive but the conclusion appears robust. The author discusses what may drive the result. Some types of learning clearly call for more suppression. This includes learning about a vaccine or treatment methods. However, when it comes to learning about contagion the problem is that, if the suppression is hard, learning may be slower (we cannot know how contagious a virus is if there is no spread).

Going beyond the analysis of the paper, the result suggests a positive externality: if one country would allow faster spreading, then all could learn about the virus by looking at one country´s experience. The question is who would want to be the one to "experiment" and bear the risk associated.

Link to paper here.

Posted by Daniel Spiro

Uppsala University

SSE-CERN Report