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This paper presents an evolutionary, game theoretic approach to simulate and study the collective outcome of public goods (PG) provisioning in an agent-based model. Using asymmetric information as the basis for decision making, distinct groups are configured to interact in an iterated N player PG game, where coevolutionary learning is used as the adaptation tool to the dynamic environment. Impact of information type, number of players, group size, rate of interaction, number of available choices, nature of PG provision, and selection schemes are studied under a variety of settings. Simulation results reveal interesting dynamics of strategy and usage profiles, level of derived welfare, and the evolution of cooperation. Analysis of simulated attributes offers a more holistic understanding into the nature of collective action and insights of how the effects of social dilemma can be mitigated. This might provide a good guide to achieve efficient PG provision in the practical context.