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Sensitivity analysis of a risk classification model for food price volatility
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-2317-9157
Dalarna University, School of Technology and Business Studies, Economics.ORCID iD: 0000-0002-9748-9572
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0003-4812-4988
2018 (English)In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 21, no 4, p. 374-382Article in journal (Refereed) Published
Abstract [en]

A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

Place, publisher, year, edition, pages
2018. Vol. 21, no 4, p. 374-382
Keywords [en]
risk; sensitivity analysis; multiple criteria; weights; decision maker; classification model; imprecision; uncertainty; data; price volatility
National Category
Economics and Business Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-28647Scopus ID: 2-s2.0-85055889650OAI: oai:DiVA.org:du-28647DiVA, id: diva2:1252277
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-11-12Bibliographically approved
In thesis
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
Open this publication in new window or tab >>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 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2018
Series
Dalarna Doctoral Dissertations in Microdata Analysis ; 8
Keywords
Coherence, Efficiency, Decision Support System, Multi-Criteria, Risk, Classification Model, Decision Makers, Judgment, Alternatives, Prediction, Data, Integrate, Imprecision, Food Security, UTADIS
National Category
Other Computer and Information Science Economics and Business
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28649 (URN)978-91-85941-79-7 (ISBN)
Public defence
2018-11-28, B311, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2018-10-30 Created: 2018-10-01 Last updated: 2019-06-17Bibliographically approved

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Laryea, RuebenCarling, KennethCialani, CatiaNyberg, Roger G.

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