Epiphany Learning for Bayesian Updating: Overcoming the Generalized Monty Hall Problem
Date Issued
2011
Date
2011
Author(s)
Chen, Wei
Abstract
Investigating methods to correct irrational behavior in a choice anomaly is central to understanding people’s choice decision. In this paper, we examined subjects’ decision in a modified version of the Monty Hall Problem (MHP) under a laboratory experimental setting. Our results show that an easier (100-door) version of the MHP would significantly raise subjects’ optimal choice (i.e. switch) rate later in the harder one (3-door) up to more than 80%, higher than most of the previous work without subject communication and/or competition. Moreover, results from estimating structural learning models using subject-level data show that the individual learning process is more likely to be an epiphany rather than a gradual one, such as reinforcement learning.
Subjects
Modified Monty Hall Problem
Bayesian Probability
Epiphany Learning
Reinforcement Learning
Experimental Economics
Type
thesis
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