Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
Jianlei Zhang, Chunyan Zhang, Tianguang Chu, Matjaz Perc

TL;DR
This paper demonstrates that applying particle swarm optimization to the spatial prisoner's dilemma significantly promotes cooperation, even under conditions that typically favor defection, offering new insights into the evolution of cooperative behavior.
Contribution
It introduces a novel application of particle swarm optimization to the spatial prisoner's dilemma, showing it can sustain cooperation in challenging environments.
Findings
High levels of cooperation achieved across various parameters
Full resolution of the prisoner's dilemma observed
Swarm-based optimization outperforms traditional strategies
Abstract
We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable…
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