Asymmetric iterated prisoner’s dilemma on weighted complex networks and evolutionary strategies analysis

Abstract

Abstract In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth, reputation, and rationality can lead to varying outcomes for similar actions. Besides, in complex social networks, an individual’s choices are frequently influenced by their neighbors. To explore the evolution of strategies in realistic settings, we conduct repeated asymmetric iterated prisoner’s dilemma experiments on weighted networks using a Memory-one strategy framework and different strategy update rules. During the strategy evolution on the network, two key strategies emerge, and we name them as ‘self-bad, partner-worse’ and the ‘altruists’. Then, we perform separate evolutionary experiments on several strong strategies on corresponding networks and find that strategy ‘self-bad, partner-worse’ can still stand out from the dominant strategies. Finally, by introducing optimization mechanisms, we increase the cooperation levels among individuals within the group. The models utilize in these studies diverge from conventional approaches, scrutinizing the evolutionary process at a macroscopic scale. These findings broaden the scope of evolutionary games and furnish a foundation for addressing real-world challenges.