Template-type: ReDif-Paper 1.0
Author-Name: Azomahou T.T. 
Author-Name: Opolot D. 
Author-workplace-name: UNU-MERIT
Title: Epsilon-stability and the speed of learning in network games
Abstract:  This paper introduces epsilon-stability as a generalization of the  concept of stochastic stability in learning and evolutionary game  dynamics. An outcome of a model of stochastic evolutionary dynamics is  said to be epsilon-stable in the long-run if for a given model of  mistakes it maximizes its invariant distribution. We construct an  efficient algorithm for computing epsilon-stable outcomes and provide  conditions under which epsilon-stability can be approximated by  stochastic stability. We also define and provide tighter bounds for  contagion rate and metastability as measures for characterizing the  short-run and medium-run behaviour of a typical stochastic evolutionary  model.    Keywords Stochastic evolution, network games, epsilon-stable sets, expected  waiting time, metastability, contagion rate.
Keywords: Stochastic and Dynamic Games; Evolutionary Games; Repeated Games; Information, Knowledge, and Uncertainty: General; 
Classification-JEL: C73; D80; .
Series:  Working Papers
Creation-Date: 2014
Number: 036
File-URL: http://pub.maastrichtuniversity.nl/ade7126f-791d-451c-b92f-6a670ad75236
File-Format: application/pdf
File-Size: 747196
Handle: RePEc:unm:unumer:2014036