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A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
Högskolan Dalarna, Akademin Industri och samhälle, Turismvetenskap.ORCID-id: 0000-0002-4278-3117
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.ORCID-id: 0000-0003-4812-4988
2018 (Engelska)Ingår i: Artikel i tidskrift (Refereegranskat) Submitted
Abstract [en]

A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

Ort, förlag, år, upplaga, sidor
2018.
Nationell ämneskategori
Annan data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-28648OAI: oai:DiVA.org:du-28648DiVA, id: diva2:1252279
Tillgänglig från: 2018-10-01 Skapad: 2018-10-01 Senast uppdaterad: 2018-10-01Bibliografiskt granskad
Ingår i avhandling
1. A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal
Öppna denna publikation i ny flik eller fönster >>A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Everyday humans need to make individual or collective decisions. Often the decisions aim at achieving multiple goals (thus involving multiple criteria) and rely on the decision maker(s)’ intuition, internal data, as well as external sources of data. Faced with a complex decision problem of this kind, it is a great challenge to decisionmakers to be logically coherent over time with regard to their preferences. To aid in achieving coherency, operation researchers and decision analysts have developed formal methods to support decision makers. One such method is the UTADIS method that serves as the workhorse for this thesis. I received the request from UN officials who had to manage the sustainable development goals while addressing the issue of food security. They wished for a decision support system (DSS) that could aid in their classification of countries to mitigate the risk of failing on food security. The virtue of the DSS should be that their expert judgment was complemented by formal methods for better risk classification. The UTADIS method was fitting for the purpose, but it lacked implementability. In particular, it required an iterative approach engaging the experts multiple times, while not readily lending itself to making use of external data, making it inefficient as a DSS. The fundamental contribution of this thesis is that I have solved these shortcomings of the UTADIS method, such that it now readily can be used in a functionally efficient way for the desired purpose of the UN. In solving these problems, it is also more broadly implementable as a DSS, as I have validated the artifact to a DSS, by use of several demonstrations and exposed it to sensitivity analysis.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2018
Serie
Dalarna Doctoral Dissertations in Microdata Analysis ; 8
Nyckelord
Coherence, Efficiency, Decision Support System, Multi-Criteria, Risk, Classification Model, Decision Makers, Judgment, Alternatives, Prediction, Data, Integrate, Imprecision, Food Security, UTADIS
Nationell ämneskategori
Annan data- och informationsvetenskap Ekonomi och näringsliv
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-28649 (URN)978-91-85941-79-7 (ISBN)
Disputation
2018-11-28, B311, Borlänge, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-10-30 Skapad: 2018-10-01 Senast uppdaterad: 2019-06-17Bibliografiskt granskad

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Laryea, RuebenFarsari, IoannaNyberg, Roger G.

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