A Prescriptive Approach to Eliciting Decision Information

Detta är en avhandling från Stockholm : Department of Computer and Systems Sciences, Stockholm University

Sammanfattning: The amount of information involved in many decision making situations has increased dramatically in recent years and support of some kind is often needed. Consequently, fields like Business Intelligence (BI) and Decision Support Systems (DSS) have advanced. Decision analysis applications belong to the latter category and aim to support decision making activities in businesses and organizations, and provide more clearly structured decision material to use as a basis for decisions. In spite of a belief in their potential, their employment is still limited in practice, which could partly be attributed to the fact that they are incomplete to support decision processes sufficiently in real settings. At present, e.g., the specification and execution of the elicitation of input data is often left to the discretion of the user. Yet, this involves quite a few problematic elements and is of importance for the quality of the process as a whole.This thesis focuses on more practically useful elicitation of information in decision analysis applications than what is offered today. A process model emphasizing the importance of structured elicitation of adequate input data throughout decision processes is also suggested. In order to further define the problematic aspects of elicitation, three empirical studies were conducted. The problems with eliciting precise decision data suggests that using imprecise values within elicitation is a more realistic and useful approach to strive for. Based on theory and the findings of the studies, a weight elicitation method for imprecise statements and noisy input was formalized into the Cardinal Rank Ordering of Criteria (CROC) method. This method is both compatible with an adapted prescriptive decision making model, focused on a more structured elicitation component, as well as algorithms for dealing with such data. The CROC method was employed and validated in two real-life cases, which is not so common within decision analysis research.

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