Journal
INFORMATION SCIENCES
Volume 585, Issue -, Pages 58-69Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.11.041
Keywords
Evolutionary game; Cooperation; Strategy evolution; Spatial games; Complex network; Prisoner's Dilemma
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Funding
- National Natural Science Foundation of China [62073174, 62073175, 91848203]
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This paper investigates the existence of cooperative strategies in diverse networked populations, which may result in a lower payoff for the focal player but also lead to an even lower payoff for their partner, thus reducing the overall benefits of the populations; these strategies are capable of winning with a high probability against opponents with an unknown strategy, and they exhibit survival advantage and robust fitness in evolutionary processes.
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. (c) 2021 Elsevier Inc. All rights reserved.
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