Uncertainty in Artificial Intelligence
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Decision Flexibility
Tom Chavez, Ross Shachter
Abstract:
The development of new methods and representations for temporal decision-making requires a principled basis for characterizing and measuring the flexibility of decision strategies in the face of uncertainty. Our goal in this paper is to provide a framework - not a theory - for observing how decision policies behave in the face of informational perturbations, to gain clues as to how they might behave in the face of unanticipated, possibly unarticulated uncertainties. To this end, we find it beneficial to distinguish between two types of uncertainty: "Small World" and "Large World" uncertainty. The first type can be resolved by posing an unambiguous question to a "clairvoyant," and is anchored on some well-defined aspect of a decision frame. The second type is more troublesome, yet it is often of greater interest when we address the issue of flexibility; this type of uncertainty can be resolved only by consulting a "psychic." We next observe that one approach to flexibility used in the economics literature is already implicitly accounted for in the Maximum Expected Utility (MEU) principle from decision theory. Though simple, the observation establishes the context for a more illuminating notion of flexibility, what we term flexibility with respect to information revelation. We show how to perform flexibility analysis of a static (i.e., single period) decision problem using a simple example, and we observe that the most flexible alternative thus identified is not necessarily the MEU alternative. We extend our analysis for a dynamic (i.e., multi-period) model, and we demonstrate how to calculate the value of flexibility for decision strategies that allow downstream revision of an upstream commitment decision.
Keywords: Temporal decision-making, decision analysis, decision theory, flexibility, planning.
Pages: 77-86
PS Link:
PDF Link: /papers/95/p77-chavez.pdf
BibTex:
@INPROCEEDINGS{Chavez95,
AUTHOR = "Tom Chavez and Ross Shachter",
TITLE = "Decision Flexibility",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "77--86"
}


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