Asymmetric Iterated Prisoner's Dilemma on BA Scale-Free Network

TL;DR

This paper investigates how asymmetric strategies evolve in a weighted BA scale-free network during repeated Prisoner's Dilemma games, revealing key strategies and increased cooperation levels.

Contribution

It introduces an analysis of memory-one strategies in asymmetric Prisoner's Dilemma on scale-free networks, identifying influential strategies and cooperation enhancement.

Findings

Memory-one strategy components significantly affect win rates

Two special strategies emerged during evolution

Cooperation levels increased among individuals

Abstract

In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth can lead to varying outcomes for similar actions. In complex social networks, individuals' choices are also influenced by their neighbors. To explore the evolution of strategies in realistic settings, we conducted repeated asymmetric prisoners dilemma experiments on a weighted BA scale-free network. Our analysis highlighted how the four components of memory-one strategies affect win rates, found two special strategies in the evolutionary process, and increased the cooperation levels among individuals. These findings offer practical insights for addressing real-world problems.