Peyton Young

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H. Peyton Young
Born (1945-03-09) March 9, 1945 (age 69)
Residence U.S., U.K.
Nationality American
Fields Economics, Game Theory
Institutions University of Oxford
Alma mater Harvard University
University of Michigan
Doctoral students Dean P. Foster
Rakesh Vohra
Known for Evolutionary Game Theory
Social Dynamics
Learning in Games
Distributive justice
Notable awards
George Hallett Award, American Political Science Association (2008)
Fellow of the British Academy (2007)
Fellow of the Econometric Society (1995)
Lester R. Ford Award, Mathematical Association of America (1976)

Hobart Peyton Young (born March 9, 1945) is an American game theorist and economist known for his contributions to evolutionary game theory and its application to the study of institutional and technological change, as well as the theory of learning in games. He holds the James Meade Professorship in economics at the University of Oxford, is a Professorial Fellow at Nuffield College and Research Professor at Johns Hopkins University. He was named a Fellow of the Econometric Society in 1995 and a Fellow of the British Academy in 2007.

Peyton Young is also a Senior Fellow for the Center on Social and Economic Dynamics at the Brookings Institution and an external faculty member at the Santa Fe Institute. He served as president of the Game Theory Society from 2006-08.[1] He has published widely on learning in games, the evolution of social norms and institutions, cooperative game theory, bargaining and negotiation, taxation and cost allocation, political representation, voting procedures, and distributive justice.

Education and career[edit]

In 1966, he graduated cum laude in General Studies from Harvard University. He completed a PhD in Mathematics at the University of Michigan in 1970, where he graduated with the Sumner B. Myers thesis prize for his work in combinatorial mathematics.

His first academic post was at the Graduate School of the City University of New York as Assistant Professor and then Associate Professor with tenure, from 1971 to 1977. From 1976 to 1982, Young was Research Scholar and Deputy Chairman of the Systems and Decision Sciences Division at the Institute for Applied Systems Analysis, Austria. He was then appointed Professor of Economics and Public Policy in the School of Public Affairs at the University of Maryland, College Park from 1992 to 1994. Young was Scott & Barbara Black Professor of Economics at the Johns Hopkins University from 1994, until moving to Oxford as James Meade Professor of Economics in 2007.


Evolutionary game theory[edit]

Conventional concepts of dynamic stability, including the evolutionarily stable strategy concept, identify states from which small once-off deviations are self-correcting. These stability concepts are not appropriate for analyzing social and economic systems which are constantly perturbed by idiosyncratic behavior and mistakes, and individual and aggregate shocks to payoffs. Building upon Freidlin and Wentzell's (1984) theory of large deviations for continuous time-processes, Dean Foster and Peyton Young (1990) developed the more powerful concept of stochastic stability: "The stochastically stable set [SSS] is the set of states such that, in the long run, it is nearly certain that the system lies within every open set containing S as the noise tends slowly to zero" [p. 221]. This solution concept created a major impact in economics and game theory after Young (1993) developed a more tractable version of the theory for general finite-state Markov chains. A state is stochastically stable if it attracts positive weight in the stationary distribution of the Markov chain. Young develops powerful graph-theoretic tools for identifying the stochastically stable states.

In an influential book, Individual Strategy and Social Structure, Young provides a clear and compact exposition of the major results in the field of stochastic evolutionary game theory, which he pioneered. He introduces his model of social interactions called 'adaptive play.' Agents are randomly selected from a large population to play a fixed game. They choose a myopic best response, based upon a random sample of past plays of the game. The evolution of the (bounded) history of play is described by a finite Markov chain. Idiosyncratic behavior or mistakes constantly perturb the process, so that every state is accessible from every other. This means that the Markov chain is ergodic, so there is a unique stationary distribution which characterizes the long-run behavior of the process.

The theory is used to show that in 2x2 coordination games, the risk-dominant equilibrium will be played virtually all the time, as time goes to infinity. It also yields a formal proof of Thomas Schelling's (1971) result that residential segregation emerges at the social level even if no individual prefers to be segregated. In addition, the theory "demonstrates how high-rationality solution concepts in game theory can emerge in a world populated by low-rationality agents" [p. 144]. In bargaining games, Young demonstrates that the Nash (1950) and Kalai-Smorodinsky (1975) bargaining solutions emerge from the decentralized actions of boundedly rational agents without common knowledge.

In more recent work, Young has applied these and related techniques to explain spatial patterns in cropsharing contracts in Illinois (Young and Burke 2001) and the diffusion of hybrid corn in the United States (Young 2009).

Learning in games[edit]

Whereas evolutionary game theory studies the behavior of large populations of agents, the theory of learning in games focuses on whether the actions of a small group of players end up conforming to some notion of equilibrium. This is a challenging problem, because social systems are self-referential: the act of learning changes the thing to be learned. There is a complex feedback between a player's beliefs, their actions and the actions of others, which makes the data-generating process exceedingly non-stationary. Young has made numerous contributions to this literature. Foster and Young (2001) demonstrate the failure of Bayesian learning rules to learn mixed equilibria in games of uncertain information. Foster and Young (2003) introduce a learning procedure in which players form hypotheses about their opponents' strategies, which they occasionally test against their opponents' past play. By backing off from rationality in this way, Foster and Young show that there are natural and robust learning procedures that lead to Nash equilibrium in general normal form games.

The recent literature on learning in games is elegantly reviewed in Young's 2004 book, Strategic Learning and its Limits.

Selected papers[edit]

  • "Learning Efficient Nash Equilibria in Distributed Systems" (with Bary S.R. Pradelski), Games and Economic Behavior, 75 (2012), 882-897.
  • "Gaming Performance Fees by Portfolio Managers" (with Dean P. Foster), forthcoming Quarterly Journal of Economics, and Working Paper 09-09, Wharton Financial Institutions Center, 2009.
  • "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence and Social Learning", American Economic Review, 99 (2009), pp. 1899–1924.
  • "Learning by Trial and Error," Games and Economic Behavior, 65 (2009), 626-643.
  • "Learning, Hypothesis Testing, and Nash Equilibrium," (with Dean P. Foster), Games and Economic Behavior, 45 (2003), 73-96.
  • "On the Impossibility of Predicting the Behavior of Rational Agents," (with Dean P. Foster), Proceedings of the National Academy of Sciences of the USA, 98, no. 22 (2001), 12848-12853.
  • "Competition and Custom in Economic Contracts: A Case Study of Illinois Agriculture," (with Mary A. Burke), American Economic Review, 91 (2001), 559-573.
  • "The Evolution of Conventions," Econometrica, 61 (1993), 57-84.
  • "An Evolutionary Model of Bargaining," Journal of Economic Theory, 59 (1993), 145-168.
  • "Stochastic Evolutionary Game Dynamics," (with Dean P. Foster), Theoretical Population Biology, 38 (1990), 219-232.


External links[edit]