Convergence in models with bounded expected relative hazard rates

Oyarzun, Carlos and Ruf, Johannes (2014) Convergence in models with bounded expected relative hazard rates. Journal of Economic Theory, 154 229-244. doi:10.1016/j.jet.2014.09.014

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Author Oyarzun, Carlos
Ruf, Johannes
Title Convergence in models with bounded expected relative hazard rates
Journal name Journal of Economic Theory   Check publisher's open access policy
ISSN 0022-0531
Publication date 2014-11-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.jet.2014.09.014
Volume 154
Start page 229
End page 244
Total pages 16
Place of publication Maryland Heights, MO, United States
Publisher Academic Press
Language eng
Abstract We provide a general framework to study stochastic sequences related to individual learning in economics, learning automata in computer sciences, social learning in marketing, and other applications. More precisely, we study the asymptotic properties of a class of stochastic sequences that take values in [0,1] and satisfy a property called “bounded expected relative hazard rates.” Sequences that satisfy this property and feature “small step-size” or “shrinking step-size” converge to 1 with high probability or almost surely, respectively. These convergence results yield conditions for the learning models in [13], [35] and [7] to choose expected payoff maximizing actions with probability one in the long run.
Keyword Hazard rate
Individual learning
Social learning
Two-armed bandit algorithm
Dynamic system
Stochastic approximation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Economics Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 10 Oct 2014, 00:13:24 EST by Alys Hohnen on behalf of School of Economics