The “self-bad, partner-worse” strategy inhibits cooperation in networked populations

Chunyan Zhang, Siyuan Liu, Zhijie Wang, Franz J. Weissing, Jianlei Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
171 Downloads (Pure)

Abstract

The emergence and maintenance of cooperation is a popular topic in studies of information sciences and evolutionary game theory. In two-player iterated games, memory in terms of the outcome of previous interactions and the strategy choices of co-players are of great referential significance for subsequent strategy actions. It is generally recognized that there is no single simple and overarching strategy whereby one player X can unilaterally achieve a higher payoff than his opponent Y, irrespective of Y's strategy and response. In this paper, we demonstrate that such strategies do exist in diverse networked populations. More precisely, (i) such strategies can obtain a low payoff for the focal player, however, they also lead to an even lower payoff for that player's partner, in turn lowering benefits of the overall populations; (ii) they are capable of winning with a high probability against opponents with an unknown strategy; and (iii) they have a survival advantage and robust fitness in terms of evolutionary processes. We refer to these as the “self-bad, partner-worse” (SBPW) strategies. Results presented here add to previous studies on strategy evolution in the context of social dilemmas and hint at some insights concerning cooperation promotion mechanisms among networked populations.

Original languageEnglish
Pages (from-to)58-69
Number of pages12
JournalInformation Sciences
Volume585
Early online date22-Nov-2021
DOIs
Publication statusPublished - Mar-2022

Keywords

  • Complex network
  • Cooperation
  • Evolutionary game
  • Prisoner's Dilemma
  • Spatial games
  • Strategy evolution

Fingerprint

Dive into the research topics of 'The “self-bad, partner-worse” strategy inhibits cooperation in networked populations'. Together they form a unique fingerprint.

Cite this