Information-Theoretic Foundation for the Weighted Updating Model
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Authors
Jesse Zinn
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Journal Article, Academic Journal
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Abstract
Weighted Updating generalizes Bayesian updating by allowing for biased beliefs through weighting the likelihood function and prior distribution with positive real exponents. I provide a rigorous foundation for the model by showing that transforming a distribution by exponential weighting (and normalizing) systematically affects the information entropy of the resulting distribution. For weights greater than one the resulting distribution has less information entropy than the original distribution, and vice versa. As the entropy of a distribution measures how informative a decision maker is treating the underlying observation(s), this result provides a useful interpretation for weighted updating.