Asynchronous Evolution of Pairs - How spatial evolution leads to inequality

We present a simple model of spatial evolution that avoids several problems that arise with more complex networks of players. We consider a world where pairs of players are matched forever. These players learn from the whole population but they are more likely to learn to strategies used by their partners. Thus, several features of spatial evolution are captured while nonlinearities that would arise with more complex networks are avoided. We can identify characteristics of evolution in networks such as stable cooperation in prisoners' dilemma games and long run exploitation among different strategies. We further discuss evolution of repeated game strategies in this framework comparing synchronous models with asynchronous ones.