Good news and bad news are still news: Experimental evidence on belief updating

Alexander Coutts

NOVAFRICA Working Paper Series from Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA

Abstract: Bayesian updating remains the benchmark for dynamic modeling under uncertainty within economics. Recent theory and evidence suggest individuals may process information in a biased manner when it relates to personal characteristics or future life outcomes. Specifically, updating has been found to be symmetric, with good news receiving more weight than bad news. I put this theory and evidence to the test, by examining information processing across multiple contexts with varying stake conditions. I do not find that good news is over-weighted relative to bad news, but in fact, I find the opposite asymmetry. However these updating patterns are present more generally, including when news is neither good nor bad. While updating across all context and stake conditions is asymmetric and conservative, posteriors remain well approximated by those calculated using Bayes rule. I investigate further possible determinants of asymmetry and conservatism, finding that the former is sensitive to signal types and the latter is driven solely by non-updates. Most importantly these patterns are present across all contexts, cautioning against the interpretation of asymmetric updating or other deviations from Bayes rule as being motivated by psychological biases.

Keywords: Beliefs; Bayes' rule; asymmetric belief updating; conservatism; overconfidence (search for similar items in EconPapers)
JEL-codes: C91 D83 D84 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2017
New Economics Papers: this item is included in nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Journal Article: Good news and bad news are still news: experimental evidence on belief updating (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:unl:novafr:wp1703

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