Evolutionary dynamics of spatial games
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Incentivizing connectivity in structured Peer-to-Peer systems
Web Intelligence and Agent Systems
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We propose a simple mechanism based on taxes and subsidies to enhance high cooperation in evolutionary networks. The interactions among agents are based on the Spatial Prisoners' Dilemma game in which each agent interacts with the same strategy with its local neighbors, collects an aggregate payoff and imitates the strategy of its best neighbor. First we study the mechanism in a regular lattice where it is well-known that the asymptotic fraction of cooperators fluctuates around a constant value for almost all starting proportions and configurations of cooperators. We also explore the mechanism in random and adaptive networks, where adaptability refers to the ability of agents to change their local neighborhood. For theses networks it has been reported that the starting proportion of cooperation has to be sufficiently high in order to obtain highly cooperative levels in the long-run time. The implementation of our mechanism produces successful results in such evolutionary networks, higher levels of cooperation are reached for all initial fractions of cooperation, including the most adverse case of just one cooperator in the network. Additionally, we observe that the network reaches a spatial configuration such that the fraction of cooperators remains in high level even when the mechanism is switched off. As a result the mechanism can work as an ignition engine to achieve highly cooperative level since it can be implemented for a finite period of time.