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Strategic experimentation

delete1999-03-01
delete345
PRE
AI
P
Patrick Bolton *
C
Christopher Harris
DOI：10.1111/1468-0262.00022delete
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Abstract

Abstract

En 中文
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.
Keywords:
multi-agent two-armed bandit
informational public good
free-rider problem
encouragement effect
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Journal

Econometrica cover
Econometrica
IF:
7.1
Papers:
3.0K
Citations:
4.3W

Organization

No organization information available