CO-EVOLUTIONARY LEARNING IN STRATEGIC ENVIRONMENTS

An interesting problem is under what circumstances will a collection of interacting agents realize efficient collective actions. This question will depend crucially on how self-interested agents interact and how they learn from each other. We model strategic interactions as dilemma games, coordination games or hawk-dove games. It is well known that the replicator dynamics based on natural selection converge to an inefficient equilibrium. In this chapter, we focus on the effect of coevolutionary learning. Each agent is modeled to learn interaction rules defined as the function of own strategy and the strategy of the neighbor. We show that a collection of interacting agents converges into equilibrium in which the conditions of efficiency and equity are satisfied. We investigate interaction rules acquired by all agents and show that they share several rules with the common features to sustain equitable social efficiency. This chapter also presents a comparative study of two evolving populations, one in a spatial environment, and the other in a small-world environment. The effect of the environment on the emergence of social efficiency is studied. The small-world environment is shown to encourage the emergence of social efficiency further than the spatial structure.