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Belief updating: Does the 'good-news, bad-news' asymmetry extend to purely financial domains?
[working paper]
Corporate Editor
Wissenschaftszentrum Berlin für Sozialforschung gGmbH
Abstract Bayes' statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Some recent research has questioned the use of Bayes' rule in descriptive models of behavior, presenting evidence that people overweight 'good news' rel... view more
Bayes' statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Some recent research has questioned the use of Bayes' rule in descriptive models of behavior, presenting evidence that people overweight 'good news' relative to 'bad news' when updating ego-relevant beliefs. In this paper, we present experimental evidence testing whether this 'good-news, bad-news' effect is present in a financial decision making context (i.e. a domain that is important for understanding much economic decision making). We find no evidence of asymmetric updating in this domain. In contrast, in our experiment, belief updating is close to the Bayesian benchmark on average. However, we show that this average behavior masks substantial heterogeneity in individual updating behavior. We find no evidence in support of a sizeable subgroup of asymmetric updators.... view less
Classification
Basic Research, General Concepts and History of Economics
Free Keywords
Bayes' rule; C11; C91; D83; belief measurement; belief updating; economic experiments; motivated beliefs; proper scoring rules
Document language
English
Publication Year
2020
City
Berlin
Page/Pages
32, 25 p.
Series
Discussion Papers / Wissenschaftszentrum Berlin für Sozialforschung, Forschungsschwerpunkt Markt und Entscheidung, Abteilung Ökonomik des Wandels, SP II 2016-309r2
Handle
https://hdl.handle.net/10419/214884
Status
Published Version; reviewed
Licence
Deposit Licence - No Redistribution, No Modifications