Co-Learning and the Evolution of Social Activity

摘要

We introduce the notion of co-learning, which refers to a process in whichseveral agents simultaneously try to adapt to one another's behavior so as to produce desirable global system properties. Of particular interest are two specific co-learning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic co-learning rule, called Higest Cumulative Reward (HCR), and show that it gives rise to quite nontrivial system dynamics. In general, we are interested in the eventual convergence of the co-learning system to desirable states, as well as in the efficiency with this convergence is attained.