2025 Zayira Ray
Julius Silver Professor, Faculty of Arts and Science,
Professor of Economics, New York University
Research Associate, NBER
Spool Member, ThReD
Research Fellow, CESifo


Department of Economics
New York University,
19 West 4th Street
New York, NY 10012, U.S.A.
debraj.ray@nyu.edu, +1 (212)-998-8906.

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Oxford University Press, 2008. This book is now open-access; feel free to download a copy, and to buy the print version if you like the book.
Three Randomly Selected Papers
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Cooperation in Community Interaction without Information Flows

(with Parikshit Ghosh), Review of Economic Studies 63, 491–519, 1996.

Summary. We study cooperative behavior in communities where the flow of information regarding past conduct is limited or missing. Players are initially randomly matched with no knowledge of each other’s past actions; they endogenously decide whether or not to continue
the repeated relationship. We define social equilibrium in such communities. Such equilibria
are characterized by an initial testing phase, followed by cooperation if the test is successful. It is precisely the presence of myopic types that permit cooperation, by raising barriers to entry into new relationships.

Maximality in the Farsighted Stable Set

(with Rajiv Vohra)   Econometrica 87(5), 1763–1779 Online Appendix.

SummaryThe stable set of von Neumann and Morgenstern can be extended to cover farsighted coalitional deviations, as proposed by Harsanyi (1974), and more recently reformulated by Ray and Vohra (2015). However,  while coalitional deviations improve on existing outcomes, coalitions might do even better by moving elsewhere. Or other coalitions might intervene to impose their favored moves. We show that every farsighted stable set satisfying some reasonable, and easily verifiable, properties is unaffected by the imposition of this stringent maximality requirement. 

Reinforcement Learning in Repeated Interaction Games

(with Jon Bendor and Dilip Mookherjee), Advances in Theoretical Economics 1, Issue 1, Article 3. Additional notes on extending the model to the probabilistic choice framework of Luce.

Summary. We study long run implications of reinforcement learning when two players repeatedly interact with one another over multiple rounds to play a finite action game. Within each round, the players play the game many successive times with a fixed set of aspirations used to evaluate payoff experiences as successes or failures. The probability weight on successful actions is increased, while failures result in players trying alternative actions in subsequent rounds. The learning rule is supplemented by small amounts of inertia and random perturbations to the states of players. Aspirations are adjusted across successive rounds on the basis of the discrepancy between the average payoff and aspirations in the most recently concluded round. We define and characterize pure steady states of this model, and establish convergence to these under appropriate conditions.