Template-type: ReDIF-Paper 1.0
Author-Name: Azomahou, T.
Author-Email: azomahou@merit.unu.edu
Author-Workplace-Name: UNU-MERIT
Author-Name: Opolot, D.
Author-Email: opolot@merit.unu.edu
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.  
Classification-JEL: C73, D80, O33
Keywords: Learning, Innovation, Stochastic evolution, network games, epsilon-stable
	 sets, expected  waiting time, metastability, contagion rate
Series: UNU-MERIT Working Papers
Creation-Date: 20140431
Number: 036
File-URL: https://unu-merit.nl/publications/wppdf/2014/wp2014-036.pdf
File-Format: application/pdf
File-Size: 747 Kb
Handle: RePEc:unm:unumer:2014036 e: R