RULES OF THUMB FOR SOCIAL-LEARNING

This paper studies agents who consider the experiences of their neighb ors in deciding which of two technologies to use. We analyze two learn ing environments, one in which the same technology is optimal for all players and another in which each technology is better for some of the m. In both environments, players use exogenously specified rules of th umb that ignore historical data but may incorporate a tendency to use the more popular technology. In some cases these naive rules can lead to fairly efficient decisions in the long run, but adjustment can be s low when a superior technology is first introduced.