Accession Number : ADA325130

Title :   Co-Learning and the Evolution of Social Activity,

Corporate Author : STANFORD UNIV CA DEPT OF COMPUTER SCIENCE

Personal Author(s) : Shoham, Yoav ; Tennenholtz, Moshe

PDF Url : ADA325130

Report Date : MAR 1994

Pagination or Media Count : 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 non-trivial 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.

Descriptors :   *LEARNING, COMPUTERIZED SIMULATION, SOCIETIES, DYNAMICS, EFFICIENCY, EVOLUTION(GENERAL), ADAPTIVE SYSTEMS, CONVERGENCE, BEHAVIOR.

Subject Categories : Psychology

Distribution Statement : APPROVED FOR PUBLIC RELEASE