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Laryea, Rueben
Publications (6 of 6) Show all publications
Laryea, R. (2018). 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. (Doctoral dissertation). Borlänge: Dalarna University
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
Laryea, R., Farsari, I. & Nyberg, R. G. (2018). A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model.
Open this publication in new window or tab >>A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model
2018 (English)In: Article in journal (Refereed) 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.

National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28648 (URN)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
Laryea, R., Carling, K. & Cialani, C. (2018). A Food Price Volatility Model for Country Risk Classification. International Journal of Risk Assessment and Management
Open this publication in new window or tab >>A Food Price Volatility Model for Country Risk Classification
2018 (English)In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241Article in journal (Refereed) Submitted
Abstract [en]

Decision makers require risk models which satisfies their preferences in decision making processes. A methodological approach to presenting a decision model that satisfies the preferences of the decision maker and aids the decision maker to classify countries into crisis groups based on the price volatility of food staple criteria is discussed in this paper. The price volatility of food staples is obtained from time series plots and a Multi-Criteria Decision Analysis method, the UTilitdditives DIScriminantes (UTADIS) classification methodological framework is applied on the price volatility data to develop a food price volatility classification model which suits the decision maker’s preferences. The methodological framework is better applied in this paper by aiding the decision maker to make informed judgements on the price volatility of food staples in predefining their risk classes. This introduces efficiency in the application of the methodological classification framework, by reducing to the barest minimum level, the misclassification errors between the decision makers preferred classification and the UTADIS method’s classification which estimates the utility function or classification model and the utility threshold or cut-off points which would classify the country alternatives into their authentic or original classes with the execution of the methodological framework just once. The resulting utility function or classification model is thus accurate enough to satisfy the preferences of the decision maker in classifying future datasets.

National Category
Economics and Business
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28646 (URN)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
Laryea, R., Carling, K., Cialani, C. & Nyberg, R. G. (2018). Sensitivity analysis of a risk classification model for food price volatility. International Journal of Risk Assessment and Management, 21(4), 374-382
Open this publication in new window or tab >>Sensitivity analysis of a risk classification model for food price volatility
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.

Keywords
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:nbn:se:du-28647 (URN)2-s2.0-85055889650 (Scopus ID)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-11-12Bibliographically approved
Laryea, R. (2013). A multi-criteria prediction model for project risk classifications. International Journal of Decision Sciences, Risk and Management, 5(1)
Open this publication in new window or tab >>A multi-criteria prediction model for project risk classifications
2013 (English)In: International Journal of Decision Sciences, Risk and Management, ISSN 1753-7169, E-ISSN 1753-7177, Vol. 5, no 1Article in journal (Refereed) Published
Abstract [en]

Project distress predictions are essential in project management. Developing appropriate methods to classify projects and building prediction models for multi-criteria decisions requires empirical methods to minimise misclassification errors. This paper carries out multi-criteria analysis to classify projects risks using a preference disaggregation method, utilités additives discriminantes – UTADIS. The UTADIS requires predefined classification which is implemented using critical path analysis. The methods are applied on three projects and result in no misclassification error and an effective prediction model.

Keywords
project risk, uncertainty, multi-criteria, classifications, utilités additives discriminantes, UTADIS, critical paths, decisions
National Category
Economics and Business
Identifiers
urn:nbn:se:du-28645 (URN)10.1504/IJDSRM.2013.057536 (DOI)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
Laryea, R. (2013). Project outcome classification with imprecise criteria information. International Journal of Applied Decision Sciences, 6(4)
Open this publication in new window or tab >>Project outcome classification with imprecise criteria information
2013 (English)In: International Journal of Applied Decision Sciences, ISSN 1755-8077, E-ISSN 1755-8085, Vol. 6, no 4Article in journal (Refereed) Published
Abstract [en]

A case in which managers have to make project outcome classification decisions with uncertainty in independently related criteria values is considered in this paper. A multi-criteria decision model is developed in this paper by selecting methods which delved into data analysis to help managers make informed classification decisions. Uncertainty in the criteria values is resolved using linear programming which enables managers to know the profit outcome of their projects for efficient resource allocation. The classification scheme from the linear programming process is used as predefined classification inputs for use in the UTilités Additives DIScriminantes (UTADIS) method, which further produces a classification model. The analysis presented a no misclassification error in the predefined classifications from the linear programming and the classifications in the UTADIS method thus further boosting the confidence managers can entrust in the resulting classification model.

Keywords
multi-criteria, classification, project outcomes, imprecision, linear programming.
National Category
Other Computer and Information Science Information Systems, Social aspects
Identifiers
urn:nbn:se:du-28644 (URN)10.1504/IJADS.2013.056867 (DOI)
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
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