EconPapers    
Economics at your fingertips  
 

Stability for best experienced payoff dynamics

William Sandholm, Segismundo Izquierdo () and Luis Izquierdo ()

Journal of Economic Theory, 2020, vol. 185, issue C

Abstract: We study a family of population game dynamics under which each revising agent randomly selects a set of strategies according to a given test-set rule; tests each strategy in this set a fixed number of times, with each play of each strategy being against a newly drawn opponent; and chooses the strategy whose total payoff was highest, breaking ties according to a given tie-breaking rule. These dynamics need not respect dominance and related properties except as the number of trials become large. Strict Nash equilibria are rest points but need not be stable. We provide a variety of sufficient conditions for stability and for instability, and illustrate their use through a range of applications from the literature.

Keywords: Evolutionary game dynamics; Best experienced payoff dynamics; Sampling dynamics; Dynamic stability (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0022053119301073
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:185:y:2020:i:c:s0022053119301073

DOI: 10.1016/j.jet.2019.104957

Access Statistics for this article

Journal of Economic Theory is currently edited by A. Lizzeri and K. Shell

More articles in Journal of Economic Theory from Elsevier
Bibliographic data for series maintained by Haili He ().

 
Page updated 2021-01-15
Handle: RePEc:eee:jetheo:v:185:y:2020:i:c:s0022053119301073