An evolutionary, game theoretic approach to the modeling, simulation and analysis of public goods provisioning under asymmetric information

The paper presents an agent-based, computational model to simulate and analyze the collective outcome of public goods provisioning under asymmetric information. Agents that embrace different information types are conceptualized and configured to interact in an N-player public goods game where each agent group adapts to the dynamic environment using co-evolutionary learning. The impact of information type, number of players, rate of interaction, group size, and the scheme of game play are studied under different settings. The simulated results reveal interesting dynamics of strategy profiles, level of public goods provisioned, and the evolution of cooperation. Analysis of these simulated attributes provides a more holistic understanding of collective action and insights into how the effects of social dilemma can be mitigated.