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Modelling Biased Judgement with Weighted Updating

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  • Zinn, Jesse

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 50310.

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Date of creation: 30 Sep 2013
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Handle: RePEc:pra:mprapa:50310

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Keywords: Bayesian Updating; Cognative Biases; Learning; Uncertainty;

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References

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  1. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, 08.
  2. Koellinger, Philipp & Minniti, Maria & Schade, Christian, 2007. ""I think I can, I think I can": Overconfidence and entrepreneurial behavior," Journal of Economic Psychology, Elsevier, vol. 28(4), pages 502-527, August.
  3. Stephanie Wang, 2012. "Speculative Overpricing in Asset Markets with Information Flows," Working Papers 489, University of Pittsburgh, Department of Economics, revised Jan 2012.
  4. Grether, David M, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, MIT Press, vol. 95(3), pages 537-57, November.
  5. S. Dellavigna., 2011. "Psychology and Economics: Evidence from the Field," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 5.
  6. Grether, David M., 1992. "Testing bayes rule and the representativeness heuristic: Some experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 17(1), pages 31-57, January.
  7. Robert Bain, 2009. "Error and optimism bias in toll road traffic forecasts," Transportation, Springer, vol. 36(5), pages 469-482, September.
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Cited by:
  1. Zinn, Jesse, 2013. "Self-Attribution Bias and Consumption," MPRA Paper 50314, University Library of Munich, Germany.

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