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Employing Cardinal Rank Ordering of Criteria in Multi-Criteria Decision Analysis
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. (Decide)
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2012 (English)In: Uncertainty Modeling in Knowledge Engineering and Decision Making: Proceedings of the 10th International FLINS Conference / [ed] Cengiz Kahraman, Etienne E. Kerre, Faik Tunc Bozbura, 2012, , 7 p.Conference paper, Published paper (Refereed)
Abstract [en]

The elicitation of preference information in multi-criteria decision analysis (MCDA) processes and the lack of practical means supporting it is a significant problem in real-life applications of MCDA. The issues at hand are problematical in a multitude of ways, but some of these issues may be remedied by accepting weaker input statements from decision-makers than what is most commonly needed, yet being able to utilize these statements for decision evaluation. In this paper, we propose a fast and practically useful weight elicitation method, which builds on the ideas of rank-order methods, but in addition take imprecise cardinal information into account.

Place, publisher, year, edition, pages
2012. , 7 p.
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, 7
Keyword [en]
multi-criteria decision analysis criteria elicitation
National Category
Social Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-75395ISBN: 978-981-4417-73-0 (print)OAI: oai:DiVA.org:su-75395DiVA: diva2:516275
Conference
The 10th International FLINS Conference, August 27-29, 2012, Istanbul, Turkey
Available from: 2012-04-18 Created: 2012-04-17 Last updated: 2013-02-11Bibliographically approved
In thesis
1. A Prescriptive Approach to Eliciting Decision Information
Open this publication in new window or tab >>A Prescriptive Approach to Eliciting Decision Information
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

Mängden information i många beslutssituationer har ökat markant under senare år och det finns ofta behov av någon form av stöd. Följaktligen har områden som Business Intelligence (BI) och Beslutsstödssystem (BSS) avancerat. Beslutsanalysverktyg tillhör den senare kategorin och syftar till att fungera som stöd vid beslutsfattande inom företag och organisationer och tillhandahålla mer strukturerat underlag för beslut. Trots en tro på deras potential, så är deras användande begränsat i praktiken, vilket delvis kan tillskrivas det faktum att de är inkompletta för att stödja beslutsprocesser i tillräcklig utsträckning i verkligheten. För närvarande förutsätts, t.ex. ofta att användaren själv klarar av att specificera och utföra utvinningen (eliciteringen) av input data. Detta involverar dock ett antal problematiska delar och dess kvalité är av vikt för hela processen.

Denna avhandling fokuserar på mer praktiskt användbar elicitering av information i beslutsanalys-applikationer än vad som finns att tillgå idag. En processmodell som betonar vikten av strukturerad elicitering av adekvata indata genom hela beslutsprocessen föreslås också. För att ytterligare definiera de problematiska aspekterna av elicitering utfördes tre empiriska studier. Problemen med att utvinna precisa beslutsdata antyder att användandet av oprecisa värden inom elicitering är en mer realistisk och användbar ansats att sträva efter. Baserat på teori och resultaten av studierna formaliserades en vikteliciterings-metod för oprecisa utlåtanden och osäkra indata i Cardinal Rank Ordering of Criteria (CROC) metoden. Metoden är både kompatibel med en anpassad preskriptiv beslutsmodell fokuserad på en mer strukturerad eliciteringskomponent samt algoritmer för att hantera denna typ av data. CROC-metoden användes och validerades i två riktiga fall, vilket inte är så vanligt inom beslutsanalys forskning.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2012. 90 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 12-008
Keyword
Decision Analysis, prescriptive, elicitation methods, multi-criteria
National Category
Social Sciences
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-75396 (URN)978-91-7447-517-3 (ISBN)
Public defence
2012-05-31, lecture room 401, Forum, Isafjordsgatan 39, Kista, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Accepted. Paper 7: Submitted. 

Available from: 2012-05-09 Created: 2012-04-17 Last updated: 2012-04-30Bibliographically approved

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