Evolutionary game dynamics in populations with different learners Journal Article

Author(s): Chatterjee, Krishnendu; Zufferey, Damien; Nowak, Martin A
Article Title: Evolutionary game dynamics in populations with different learners
Affiliation IST Austria
Abstract: We study evolutionary game theory in a setting where individuals learn from each other. We extend the traditional approach by assuming that a population contains individuals with different learning abilities. In particular, we explore the situation where individuals have different search spaces, when attempting to learn the strategies of others. The search space of an individual specifies the set of strategies learnable by that individual. The search space is genetically given and does not change under social evolutionary dynamics. We introduce a general framework and study a specific example in the context of direct reciprocity. For this example, we obtain the counter intuitive result that cooperation can only evolve for intermediate benefit-to-cost ratios, while small and large benefit-to-cost ratios favor defection. Our paper is a step toward making a connection between computational learning theory and evolutionary game dynamics.
Keywords: Evolutionary game theory; Direct reciprocity (Prisoner's dilemma); Learning theory
Journal Title: Journal of Theoretical Biology
Volume: 301
ISSN: 0022-5193
Publisher: Elsevier  
Date Published: 2012-05-21
Start Page: 161
End Page: 173
DOI: 10.1016/j.jtbi.2012.02.021
Notes: We thank the anonymous reviewers for their comments that helped us to improve the paper significantly.nSupports from the John Templeton Foundation and the NSF/NIH joint program in mathematical biology (NIH grant R01GM078986), ERC Start grant (279307: Graph Games), FWF NFN Grant No. S11407-N23 (RiSE), FWF Grant No. P 23499-N23, and Microsoft faculty fellows awards are gratefully acknowledged.
Open access: yes (repository)