Uncertainty in Artificial Intelligence
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A Game-Theoretic Analysis of Updating Sets of Probabilities
Peter Grunwald, Joseph Halpern
We consider how an agent should update her uncertainty when it is represented by a set P of probability distributions and the agent observes that a random variable X takes on value x, given that the agent makes decisions using the minimax criterion, perhaps the best-studied and most commonly-used criterion in the literature. We adopt a game-theoretic framework, where the agent plays against a bookie, who chooses some distribution from P. We consider two reasonable games that differ in what the bookie knows when he makes his choice. Anomalies that have been observed before, like time inconsistency, can be understood as arising because different games are being played, against bookies with different information. We characterize the important special cases in which the optimal decision rules according to the minimax criterion amount to either conditioning or simply ignoring the information. Finally, we consider the relationship between conditioning and calibration when uncertainty is described by sets of probabilities.
Keywords: null
Pages: 240-247
PS Link:
PDF Link: /papers/08/p240-grunwald.pdf
AUTHOR = "Peter Grunwald and Joseph Halpern",
TITLE = "A Game-Theoretic Analysis of Updating Sets of Probabilities",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "240--247"

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