Modelling Biased Judgement with Weighted Updating

Jesse Zinn

MPRA Paper from University Library of Munich, Germany

Abstract: The weighted updating model is a generalization of Bayesian updating that allows for biased beliefs by weighting the functions that constitute Bayes' rule with real exponents. I provide an axiomatic basis for this framework and show that weighting a distribution affects the information entropy of the resulting distribution. This result provides the interpretation that weighted updating models biases in which individuals mistake the information content of data. I augment the base model in two ways, allowing it to account for additional biases. The first augmentation allows for discrimination between data. The second allows the weights to vary over time. I also find a set of sufficient conditions for the uniqueness of parameter estimation through maximum likelihood, with log-concavity playing a key role. An application shows that self attribution bias can lead to optimism bias.

Keywords: Bayesian Updating; Cognative Biases; Learning; Uncertainty (search for similar items in EconPapers)
JEL-codes: C02 D03 (search for similar items in EconPapers)
Date: 2013-09-30
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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https://mpra.ub.uni-muenchen.de/61403/1/MPRA_paper_61403.pdf revised version (application/pdf)

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