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Degree

Doctor of Philosophy, Ohio State University, Computer and Information Science, 2001.

Abstract

Multiple Criteria Decision Making (MCDM) problems involve the selection of “good” alternatives from a set of alternatives, each of which is evaluated along multiple, and potentially conflicting, criteria. The criteria are intended to reflect the dimensions of outcome that matter to the decision-maker (DM). The decision-making process should select alternatives which optimize the outcomes most desired by the DM. Decision Support Systems (DSSs) are aids that enhance the decision-making capabilities of the DM in various ways. The DM may be modeled as a holder of preferences of various kinds. Decision support, then, entails the elicitation of these preferences and their application to the set of alternatives at hand. Ideal DSSs, in this view, must allow for the natural, accurate, and complete expression of preferences by the DM and apply, or help the DM apply, these preferences. Another aspect of decision-making is the wide variability in what we might call decision situations. These situations are characterized by the differences in the degrees to which optimality is essential to the DM, the time-pressure under which the decision is being made, the degree of pruning desired, the presence of uncertainty in criteria values, etc. DSSs that provide situation-specific support are more valuable. In this work, we focus on the Seeker-Filter-Viewer (S-F-V) family of architectures and on their applicability as decision support architectures. The generic version of this architecture makes use of the Pareto Dominance Filter to eliminate suboptimal alternatives. We explore Tolerance-Based Dominance Filters (TBDFs), which are based on decision rules similar to the Dominance rule but contain criteria-specific tolerances in the rule clauses. We analyze the applicability of TBDFs to a class of decision situations, with and without uncertainty in criteria values, and in the presence of a number of user-needs and other problem characteristics. The goal is to develop a framework for mapping decision situations to appropriate instantiations of the S-F-V architecture. We present such a framework for the Filters and decision situations we consider in the dissertation. By using such a framework, the S-F-V architecture can cater to a larger class of decision problems and DMs than the earlier versions.

Keywords

multiple criteria decision making; decision making under uncertainty; Pareto optimality with tolerances

Advisor

Chandrasekaran Balakrishnan

Pages

184p.

Document number: osu1005939267
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=osu1005939267

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