EconPapers    
Economics at your fingertips  
 

Preferential learning and memory resolve social dilemma

Chenglie Du and Jianliang Li

Chaos, Solitons & Fractals, 2018, vol. 110, issue C, 16-19

Abstract: Cooperation is widespread in society, thus how to explain this phenomenon has become one open question. According to empirical experience, preferential learning and memory seem to be two effective ways to this issue, which, however, still needs validation in scientific research. Motivate by this point, we consider one-step memory and preference learning (i.e. learning the strategy of subject performing best, which is tuned by a preferential parameter α) in prisoner's dilemma game. α=0 enables the model going back to control treatment where objects randomly selected. While for α > 0, individuals prefer objects that perform better. Compared with control treatment, we find that increasing preferential parameter α can promote cooperative behavior monotonously. In particular, the larger the value of α, the stronger and more compact clusters they can form. Finally, in order to investigate the robustness of this mechanism, we also study the evolution of cooperation in small-world network and random regular network.

Keywords: One-step memory; Preference selection; Cooperation; Evolution game (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077918301085
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:chsofr:v:110:y:2018:i:c:p:16-19

DOI: 10.1016/j.chaos.2018.03.012

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().

 
Page updated 2025-03-19
Handle: RePEc:eee:chsofr:v:110:y:2018:i:c:p:16-19