Uncertainty in Artificial Intelligence
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When Ignorance is Bliss
Peter Grunwald, Joseph Halpern
It is commonly-accepted wisdom that more information is better, and that information should never be ignored. Here we argue, using both a Bayesian and a non-Bayesian analysis, that in some situations you are better off ignoring information if your uncertainty is represented by a set of probability measures. These include situations in which the information is relevant for the prediction task at hand. In the non-Bayesian analysis, we show how ignoring information avoids dilation, the phenomenon that additional pieces of information sometimes lead to an increase in uncertainty. In the Bayesian analysis, we show that for small sample sizes and certain prediction tasks, the Bayesian posterior based on a noninformative prior yields worse predictions than simply ignoring the given information.
Keywords: null
Pages: 226-234
PS Link: http://www.cwi.nl/~pdg/ftp/ignorance.ps
PDF Link: /papers/04/p226-grunwald.pdf
AUTHOR = "Peter Grunwald and Joseph Halpern",
TITLE = "When Ignorance is Bliss",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "226--234"

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