Confidence Biases and Learning among Intuitive Bayesians
Louis Lévy-Garboua,
Muniza Askari and
Marco Gazel
CIRANO Working Papers from CIRANO
Abstract:
We design a double-or-quits game to compare the speed of learning one’s specific ability with the speed of rising confidence as the task gets increasingly difficult. We find that people on average learn to be overconfident faster than they learn their true ability and we present a simple Bayesian model of confidence which integrates these facts. We show that limited discrimination of objective differences, myopia, and uncertainty about one’s true ability to perform a task in isolation can be responsible for large and robust confidence biases, namely the hard-easy effect, the Dunning-Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners. Moreover, these biases are likely to persist since the Bayesian aggregation of past information consolidates the accumulation of errors and the perception of contrarian illusory signals generates conservatism and under-reaction to events. Taken together, these two features may explain why intuitive Bayesians make systematically wrong predictions of their own performance.
Keywords: confidence biases; Bayesian learning; double or quits experimental game; hard-easy effect; Dunning-Kruger effect; illusory signals (search for similar items in EconPapers)
JEL-codes: C11 C91 D03 (search for similar items in EconPapers)
Date: 2015-11-30
New Economics Papers: this item is included in nep-exp
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https://cirano.qc.ca/files/publications/2015s-51.pdf
Related works:
Journal Article: Confidence biases and learning among intuitive Bayesians (2018) 
Working Paper: Confidence Biases and Learning among Intuitive Bayesians (2015) 
Working Paper: Confidence Biases and Learning among Intuitive Bayesians (2015) 
Working Paper: Confidence Biases and Learning among Intuitive Bayesians (2015) 
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Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2015s-51
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