A Deterministic Algorithm for Solving Imprecise Decision Problems

AAAI Conferences

Today there are numerous tools for decision analysis, suitable both for human and artificial decision makers. Most of these tools require the decision maker to provide precise numerical estimates of probabilities and utilities. Furthermore, they lack the capability to handle inconsistency in the decision models, and will fail to deliver an answer unless the formulation of the decision problem is consistent. In this paper we present an algorithm for evaluating imprecise decision problems expressed using belief distributions, that also can handle inconsistency in the model. The same algorithm can be applied to decision models where probabilities and utilities are given as intervals or point values, which gives us a general method for evaluating inconsistent decision models with varying degree of expressiveness.

Globalisation of Belief Distributions

AAAI Conferences

The probability and utility estimates involw d in such t situation is expressed tm sets of distributions, tel)resenting beliefs in various vectors in tim decision space.

Decision-Analytic Approaches to Operational Decision Making: Application and Observation

arXiv.org Artificial Intelligence

Decision analysis (DA) and the rich set of tools developed by researchers in decision making under uncertainty show great potential to penetrate the technological content of the products and services delivered by firms in a variety of industries as well as the business processes used to deliver those products and services to market. In this paper I describe work in progress at Sun Microsystems in the application of decision-analytic methods to Operational Decision Making (ODM) in its World-Wide Operations (WWOPS) Business Management Group. Working with membersof product engineering, marketing, and sales, operations planners from WWOPS have begun to use a decision-analytic framework called SCRAM (Supply Communication/Risk Assessment and Management) to structure and solve problems in product planning, tracking, and transition. Concepts such as information value provide a powerful method of managing huge information sets and thereby enable managers to focus attention on factors that matter most for their business. Finally, our process-oriented introduction of decision-analytic methods to Sun managers has led to a focused effort to develop decision support software based on methods from decision making under uncertainty.