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The theory of learning in games

TLDR
Fudenberg and Levine as discussed by the authors developed an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time.
Abstract
In economics, most noncooperative game theory has focused on equilibrium in games, especially Nash equilibrium and its refinements. The traditional explanation for when and why equilibrium arises is that it results from analysis and introspection by the players in a situation where the rules of the game, the rationality of the players, and the players' payoff functions are all common knowledge. Both conceptually and empirically, this theory has many problems. In The Theory of Learning in Games Drew Fudenberg and David Levine develop an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time. The models they explore provide a foundation for equilibrium theory and suggest useful ways for economists to evaluate and modify traditional equilibrium concepts.

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Consensus and Cooperation in Networked Multi-Agent Systems

TL;DR: A theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees is provided.
Book

Prediction, learning, and games

TL;DR: In this paper, the authors provide a comprehensive treatment of the problem of predicting individual sequences using expert advice, a general framework within which many related problems can be cast and discussed, such as repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems.
Journal ArticleDOI

Evolutionary games on graphs

György Szabó, +1 more
- 01 Jul 2007 - 
TL;DR: The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.
Proceedings Article

Online convex programming and generalized infinitesimal gradient ascent

TL;DR: An algorithm for convex programming is introduced, and it is shown that it is really a generalization of infinitesimal gradient ascent, and the results here imply that generalized inf initesimalgradient ascent (GIGA) is universally consistent.