Individual Heterogeneous Learning with Global Centrality in Prisoner Dilemma Evolutionary Game on Complex Network

Authors

Zundong Zhang^{1, 2, *,} , Yifang Zhang^3, , William Danziger⁴

¹College of Electrical and Control Engineering, North China University of Technology, No. 5 Jinyuanzhuang Road, Beijing, Shijingshan, China†

²Department of Civil and Environment Engineering, University of Washington, 3760 E. Stevens Way NE, Seattle, WA, US‡

³College of Electrical and Control Engineering, North China University of Technology, No. 5 Jinyuanzhuang Road, Beijing, Shijingshan, China

⁴Department of Civil and Environmental Engineering, University of Washington, 121G More Hall, Seattle, WA, US

^†

The permanent address of the author.

^‡

The present address of the author.

^*Corresponding author. Email: zyfncut@163.com

DOI

10.2991/ijcis.d.200603.002How to use a DOI?

Keywords

Evolution game; Individual heterogeneity; Global centrality; Cooperation rate

Abstract

The influence of individual heterogeneity on the evolutionary game has been studied extensively in recent years. Whereas many theoretical studies have found that the heterogeneous learning ability effects cooperation rate, the individual learning ability in networks is still not well understood. It is known that an individual's learning ability is influenced not only by its first order neighbors, but also by higher order individuals, and even by the whole network. At present, existing methods to represent individual learning ability are based on degree centrality, resulting in ignoring the global centrality of nodes. In this paper, we design a method for describing the heterogeneous learning ability by taking advantage of a pre-factor θx related to the node betweenness. And a parameter α is used to tune θx. Experiments show that individual heterogeneous learning ability is effected by global information. Our findings provide a new perspective to understand the important influence of the global attributes of nodes on the evolutionary game.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Zundong Zhang
AU  - Yifang Zhang
AU  - William Danziger
PY  - 2020
DA  - 2020/06/17
TI  - Individual Heterogeneous Learning with Global Centrality in Prisoner Dilemma Evolutionary Game on Complex Network
JO  - International Journal of Computational Intelligence Systems
SP  - 698
EP  - 705
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200603.002
DO  - 10.2991/ijcis.d.200603.002
ID  - Zhang2020
ER  -