Analysis and control of strategic interactions in finite heterogeneous populations under best-response update rule

For a finite, well-mixed population of heterogeneous agents playing evolutionary games choosing to cooperate or defect in each round of the game, we investigate, when agents update their strategies in each round using the myopic best-response rule, how the number of cooperating agents changes over time and demonstrate how to control that number by changing the agents´ payoff matrices. The agents are heterogeneous in that their payoff matrices may differ from one another; we focus on the specific case when the payoff matrices, fixed throughout the evolution, correspond to prisoner´s dilemma or snowdrift games. To carry out stability analysis, we identify the system´s absorbing states when taking the number of cooperating agents as a random variable of interest. It is proven that when all the agents update frequently enough, the reachable final states are completely determined by the available types of payoff matrices. As a further step, we show how to control the final state by changing at the beginning of the evolution, the types of the payoff matrices of a group of agents.