Evolving Cooperation in the Spatial N-player Snowdrift Game

The Snowdrift game is a well-known social dilemma model frequently used in evolutionary game theory to investigate the emergence of cooperative behaviour under different biologically or socially plausible conditions. In this paper, we examine a multi-player version of the Snowdrift game where (i) the agents playing the game are mapped to the nodes of a regular two-dimensional lattice, (ii) the number of rounds of the game Varies from a "one-shot" version to a fixed number of repeated interactions, and (iii) a genetic algorithm is used to evolve agent actions (strategy update) over a fixed number of generations. Comprehensive Monte Carlo simulation experiments show that cooperative behaviour is promoted in the multi-player iterated Snowdrift game. This emergent behaviour may be attributed to the combination of spatial reciprocity, based on the inherent capabilities of the genetic algorithm to explore the diverse set :3 of agents' strategies, and repeated interactions. The simulation results also uncover some interesting findings regarding the effect of repeated interactions in the game.