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Game Theory & Math Economics: Ali Nageeb (Pennsylvania State University) - "Laws, Norms, and Authority: Self-Enforcement Against Coalitional Deviations in Repeated Games" (joint with Ce Liu) | Einstein Institute of Mathematics

Game Theory & Math Economics: Ali Nageeb (Pennsylvania State University) - "Laws, Norms, and Authority: Self-Enforcement Against Coalitional Deviations in Repeated Games" (joint with Ce Liu)

Date: 
Sun, 26/03/201716:00-17:00
Location: 
Elath Hall, 2nd floor, Feldman Building, Edmond J. Safra Campus
A long-standing tradition models legal enforcement as being distinct from community enforcement, whereby individuals follow the law because legal enforcement constrains the actions that players may take or fixes their payoffs from their actions.  But this distinction is elusive:  after all, laws are enforced by other individuals, who also require incentives to fulfill  their role.  This paper views the law as being synonymous with a norm: it offers a prescription for behavior today with a shared understanding of the punishments that might follow in the future if the law is violated.  Of course, this shared understanding should not ward off only individual deviations but also coalitional deviations insofar as groups  may find ways to deviate from the law. Our paper studies what a self-enforcing norm can implement despite the possibility for individual and coalitional  deviations.   We  find that  in settings  with  perfect  monitoring,  a self- enforcing norm can implement any payoff that is feasible and individually rational so long as players are perfectly patient.  This folk theorem coincides with that for sub- game perfect equilibria in repeated games of perfect monitoring, despite the potential for coalitional deviations.  However, once coalitions can make secret transfers to each other,  a self-enforcing norm cannot implement payoffs beyond the core of the stage- game.  Apart from offering a perspective on challenges introduced by the possibility for secret transfers,  our paper offers a novel recursive approach to model coalitional reasoning in repeated interactions.