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Approximation of (k,t)-robust Equilibria

Published: 20 July 2016 Publication History

Abstract

Game theory models strategic and conflicting situations and offers several solution concepts that are known as game equilibria, among which the Nash equilibrium is probably the most popular one. A less known equilibrium, called the (k,t)-robust equilibrium, has recently been used in the context of distributed computing. The (k,t)-robust equilibrium combines the concepts of k-resiliency and t-immunity: a strategy profile is k-resilient if there is no coalition of k players that can benefit from improving their payoffs by collective deviation, and it is t-immune if any action of any t players does not decrease the payoffs of the others. A strategy profile is (k,t)-robust if it is both k-resilient and t-immune. In this paper an evolutionary approach of approximating (k,t)-robust equilibria is proposed. Numerical experiments are performed on a game that models node behavior in a distributed system.

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cover image ACM Conferences
GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016
July 2016
1196 pages
ISBN:9781450342063
DOI:10.1145/2908812
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Published: 20 July 2016

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Author Tags

  1. (k
  2. distributed systems
  3. game theory
  4. t)-robust equilibria

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GECCO '16
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GECCO '16: Genetic and Evolutionary Computation Conference
July 20 - 24, 2016
Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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