Individual Strategy and Social Structure

By Peyton Young. 2020

1,590 citations recorded by the source corpus.

Suggests a reorientation of game theory in which players are not hyper-rational and knowledge is incomplete; postulates a simple adaptive learning process; and applies this framework to the study of social and economic institutions. Discusses learning; dynamic and stochastic stability; adaptive learning in small games; variations on the learning process; local interaction; equilibrium and disequilibrium selection in general games; bargaining; and contracts. Young is Scott and Barbara Black Professor of Economics at Johns Hopkins University. Bibliography; index.

Subfields: Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0b6ef126663fd462580117f0fa8ad9b5a92d1335

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Citations in the corpus

Creating Political Legitimacy (2009, background): “Moreover, they constantly “adjust their behavior based on what they think other agents are going to do, and these expectations are generated endogenously by information about what other agents have done in the past” (Young, 1998, p. 29).”
Learning to Play Approximate Nash Equilibria in Games with Many Players (2004, background): “Illustrations of how adaptive play (similar to an innovation dynamic) need not converge to a Nash equilibrium are provided by, amongst others, Young (1993, 2001a).”
Joint Strategy Fictitious Play with Inertia for Potential Games (2005, background): “Another learning algorithm convergent in weakly acyclic games is adaptive play [21], [11] where players have finite recall.”
The Complexity Era in Economics (2011, background): “14 The work in game theory (by economists such as Young, 1998 ) is pushing rationality to its limits to demonstrate the importance of the expectations and the information environment in people's decisions.”
The Evolution of Policy Ideas: Tax Policy in the 20th Century (2003, background): “But, it does help us understand exactly how the ‘history of play’ affects the current choices on offer (Young 1998).”
An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1 (2005, methodology): “Our modeling approach is a hybrid between the cognitive agent approaches in computer science (Conte and Castelfranchi 1995; Ferber 1999; Muller 1996; Conte 1999) and the models of cellular automata networks which are more inspired by physics (Weisbuch 1991; Blume 1993; Young 1998).”
Maximizing the spread of influence through a social network (2003, background): “[5, 15, 18, 19, 21, 25, 28, 29, 31, 32, 33])but the following Linear Threshold Model lies at the core of most subsequent generalizations.”
Rational choice theory (2013, background): “Furthermore, several types of backward-looking rationality are assumed in social learning models (Fudenberg and Levine, 1998; Young, 1998).”