Play locally, learn globally: Group selection and structural basis of cooperation

In group-structured populations, altruistic cooperation among unrelated group members may be sustainable even when the evolution of behavioral traits is governed by a payoff-based replicator dynamic. This paper explores the importance in this dynamic of two aspects of group structure: global or local interaction in a public goods game and global or local cultural transmission (learning) of behavioral traits. To clarify the underlying dynamic, I derive an extension of the Price equation for the decomposition of changes in the population frequency of a binary trait. I use this to analyze the effect of different structures of interaction and learning on within- and between-group variances of the frequency of cooperative behaviors and thereby on the evolution of cooperation. Of the four population structures given by global/local learning and global/local interaction, local interaction with global learning provides the most favorable environment for the evolution of cooperation. This combination of learning and interaction structures supports a high level of between-group variance in the frequency of cooperative types, so that most cooperators benefit from being in groups composed mostly of cooperators. However, while global learning is essential to the evolution of cooperation, cooperation is more robust when learning is not entirely global because local learning process, ironically, limits the extent to which defectors can free ride on cooperative group members.