BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-94-1511
       ENTRY:: April 19, 1994
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Co-Learning and the Evolution of Social Acitivity
        TYPE:: Technical Report
      AUTHOR:: Shoham, Yoav
      AUTHOR:: Tennenholtz, Moshe
       PAGES:: 37
    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.
       NOTES:: [Adminitrivia V1/Prg/19940419]
         END:: STAN//CS-TR-94-1511