Word-of-mouth learning

This paper analyzes a model of rational word-of-mouth learning, in which successive generations of agents make once-and-for-all choices between two alternatives. Before making a decision, each new agent samples N old ones and asks them which choice they used and how satisfied they were with it. If (a) the sampling rule is "unbiased" in the sense that the samples are representative of the overall population, (b) each player samples two or more others, and (c) there is any information at all in the payoff observations, then in the long run every agent will choose the same thing. If in addition the payoff observation is sufficiently informative, the long-run outcome is efficient. We also investigate a range of biased sampling rules, such as those that over-represent popular or successful choices, and determine which ones favor global convergence towards, efficiency. (C) 2003 Elsevier Inc. All rights reserved.