Report Number: CS-TR-94-1511
Institution: Stanford University, Department of Computer Science
Title: Co-Learning and the Evolution of Social Acitivity
Author: Shoham, Yoav
Author: Tennenholtz, Moshe
Date: April 1994
Abstract: We introduce the notion of co-learning, which refers to a process in which several 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 Highest 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 which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations.