Anticipatory learning in general evolutionary games

Gürdal Arslan*, Jeff S. Shamma

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

We investigate the problem of convergence to Nash equilibrium for learning in games. Prior work demonstrates how various learning models need not converge to a Nash equilibrium strategy and may even result in chaotic behavior. More recent work demonstrates how the notion of "anticipatory" learning, or, using more traditional feedback control terminology ,"lead compensation", can be used to enable convergence through a simple modification of existing learning models. In this paper, we show that this approach is broadly applicable to a variety of evolutionary game models. We also discuss single population evolutionary models. We introduce "anticipatory" replicator dynamics and discuss the relationship to evolutionary stability.

Original languageEnglish (US)
Title of host publicationProceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
Pages6289-6294
Number of pages6
StatePublished - 2006
Externally publishedYes
Event45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States
Duration: Dec 13 2006Dec 15 2006

Other

Other45th IEEE Conference on Decision and Control 2006, CDC
CountryUnited States
CitySan Diego, CA
Period12/13/0612/15/06

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety

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