The theory of learning in games has extensively studied situations where agents respond dynamically to each other in light of a fixed utility function . However, in many settings of interest, agent utility functions themselves vary as a result of past agent choices . For example, a highly prevalent virus mayincentivize individuals to wear masks, but extensive adoption of mask-wearing reduces virus prevalence which in turn reduces individual incentives . We develop a general frameworkusing probabilistic coupling methods that can be used to derive thestochastically stable states of log-linear learning in certain games . We then apply this framework to asimple dynamic game-theoretic model of social precautions in an epidemic and give conditions under which maximally-cautious social behavior in this model is

Author(s) : Brandon C. Collins, Lisa Hines, Gia Barboza, Philip N. Brown

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Keywords : social - reduces - virus - agent - utility -

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