Co-evolutionary Learning in the N-player Iterated Prisoner's Dilemma with a Structured Environment
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Co-evolution of agent strategies in N-player dilemmas
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A multimodal problem for competitive coevolution
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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The evolution of strategies in iterated multi-player social dilemma games is studied on small-world networks. Two different games with varying reward values - the N-player Iterated Prisoner's Dilemma (N-IPD) and the N-player Iterated Snowdrift game (N-ISD) - form the basis of this study. Here, the agents playing the game are mapped to the nodes of different network architectures, ranging from regular lattices to small-world networks and random graphs. In a given game instance, the focal agent participates in an iterative game with N-1 other agents drawn from its local neighbourhood. We use a genetic algorithm with synchronous updating to evolve agent strategies. Extensive Monte Carlo simulation experiments show that for smaller cost-to-benefit ratios, the extent of cooperation in both games decreases as the probability of re-wiring increases. For higher cost-to-benefit ratios, when the re-wiring probability is small we observe an increase in the level of cooperation in the N-IPD population, but not the N-ISD population. This suggests that the small-world network structure with small re-wiring probabilities can both promote and maintain higher levels of cooperation when the game becomes more challenging.