Title
Word-of-mouth learning
Abstract
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.
Year
DOI
Venue
2004
10.1016/S0899-8256(03)00048-4
Games and Economic Behavior
Keywords
Field
DocType
word-of-mouth,d83,herding,imitation,social learning,c72,satisfiability,word of mouth,biased sampling
Population,Mathematical economics,Sampling bias,Herding,Word of mouth,Imitation,Sampling (statistics),Social learning,Mathematics,Stochastic game
Journal
Volume
Issue
ISSN
46
1
Games and Economic Behavior
Citations 
PageRank 
References 
42
11.79
0
Authors
2
Name
Order
Citations
PageRank
Abhijit Banerjee17318.49
Drew Fudenberg217544.93