Observational and Reinforcement Pattern-learning: An Exploratory Study

Nobuyuki Hanaki, Alan Kirman and Paul Pezanis-Christou

No 2016-24, GREDEG Working Papers from Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France

Abstract: We examine, experimentally and theoretically in a very simple multi-armed bandit framework, how individuals learn about an undisclosed inter-temporal payoff structure. We propose a baseline reinforcement learning model that allows for pattern-recognitions and associated change in the strategy space, as well as its three augmented versions that accommodate observational learning from the actions and/or payoffs of another player with whom they are matched. The models reproduce the distributional properties of observed discovery times well. Our study further shows that observing another's actions and/or payoffs improves discovery compared to the baseline case when one of the pair discovered the hidden pattern.

Keywords: Multi-armed bandit; reinforcement learning; eureka moment; pay-off patterns; observational learning (search for similar items in EconPapers)
JEL-codes: D81 D83 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2016-08, Revised 2017-06
New Economics Papers: this item is included in nep-cbe, nep-cse and nep-exp
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http://195.220.190.85/GREDEG-WP-2016-24.pdf Revised version, 2017-06 (application/pdf)

Related works:
Journal Article: Observational and reinforcement pattern-learning: An exploratory study (2018)
Working Paper: Observational and reinforcement pattern-learning: An exploratory study (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:gre:wpaper:2016-24

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