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Strategic Experimentation
Author(s) -
Bolton Patrick,
Harris Christopher
Publication year - 1999
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.1111/1468-0262.00022
Subject(s) - set (abstract data type) , order (exchange) , computer science , free rider problem , markov chain , management science , face (sociological concept) , operations research , mathematical economics , economics , microeconomics , public good , engineering , machine learning , social science , finance , sociology , programming language
This paper extends the classic two‐armed bandit problem to a many‐agent setting in which N players each face the same experimentation problem. The main change from the single‐agent problem is that an agent can now learn from the current experimentation of other agents. Information is therefore a public good, and a free‐rider problem in experimentation naturally arises. More interestingly, the prospect of future experimentation by others encourages agents to increase current experimentation, in order to bring forward the time at which the extra information generated by such experimentation becomes available. The paper provides an analysis of the set of stationary Markov equilibria in terms of the free‐rider effect and the encouragement effect.