Evolutionary Stable Resource Pricing Strategies∗

Enterprises currently employ Cloud services to improve the scalability of their services and resource providers strategically price resources to maximize their utilities. While Nash equilibrium is the dominant concept for studying such kind of interaction, evolutionary game theory seems more appropriate for modeling agents’ strategic interactions as it relaxes many strong assumptions. This paper applies evolutionary dynamics to generate resource providers’ evolutionary stable strategies. We present a sequential monte carlo approach for simulation of multi-population evolutionary dynamics in which each agent’s strategy space is continuous. We use resampling and Gaussian smoothing to prevent degeneration of particle samples. Simulation results show that the proposed approach always converges to evolutionary stable strategies. Our approach is general in that it can be used to generate agents’ evolutionary stable strategies for other resource allocation games.