Confidence biases and learning among intuitive Bayesians

Autor: Muniza Askari, Marco Gazel, Louis Lévy-Garboua
Přispěvatelé: Centre interuniversitaire de recherche en analyse des organisations (CIRANO), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Paris School of Economics (PSE), École des Ponts ParisTech (ENPC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-École des hautes études en sciences sociales (EHESS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'économie de la Sorbonne (CES), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
FOS: Computer and information sciences
Computer science
Dunning–Kruger effect
media_common.quotation_subject
Bayesian probability
General Decision Sciences
Conservatism
Bayesian inference
experimental game
050105 experimental psychology
Confidence biases
Task (project management)
Contrarian illusory signals
Double or quits
Arts and Humanities (miscellaneous)
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Perception
0502 economics and business
FOS: Mathematics
Developmental and Educational Psychology
Learning
0501 psychology and cognitive sciences
050207 economics
Applied Psychology
media_common
Other Statistics (stat.OT)
Probability (math.PR)
05 social sciences
Contrarian
Intuitive-Bayesian
General Social Sciences
[SHS.ECO]Humanities and Social Sciences/Economics and Finance
Computer Science Applications
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]
Statistics - Other Statistics
Doubt
Hard–easy effect
General Economics
Econometrics and Finance

Mathematics - Probability
Cognitive psychology
Zdroj: Theory and Decision
Theory and Decision, Springer Verlag, 2017
ISSN: 0040-5833
1573-7187
Popis: 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 an intuitive-Bayesian model of confidence which integrates confidence biases and learning. Uncertainty about one's true ability to perform a task in isolation can be responsible for large and stable confidence biases, namely limited discrimination, the hard--easy effect, the Dunning--Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners who rely only on sequentially perceived performance cues and contrarian illusory signals induced by doubt. 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.
Theory and Decision, Springer Verlag, 2017
Databáze: OpenAIRE