Multi-criteria Decision Analysis and Support Systems
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ItemA Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach( 2019-01-08)Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed.
ItemGamification of The Future: An Experiment on Gamifying Education of Forecasting( 2019-01-08)In this study, we developed a gamied learning platform called F-LauReLxp that employed three gamification strategies (called Horses for Courses, JudgeIt and Metrics to Escape) to help educate statistical, judgmental forecasting and forecasting accuracy respectively. This study presents a quantitative analysis of experimental design concerning learning performance of 261 students of an undergraduate and a MBA course. Treatment and control groups were compared in a series of experiments. The results show that using gamified applications as a complementary teaching tool in a forecasting course had a positive impact on students’ learning performance.
ItemAssessment of Multi-Criteria Preference Measurement Methods for a Dynamic Environment( 2019-01-08)Multi-criteria decision analysis is required in various domains where decision making reoccurs as part of a longer-term process. When the decision context changes or the preferences evolve due to process dynamics, one-shot preference measurement is not sufficient to build an adequate basis for decision making. Process dynamics require taking into account the dimension of time. We investigate six interactive preference measurement methods providing the possibility to assess alternatives in terms of utility for an individual decision maker, whether they are suitable for dynamic preference adjustment. We use a mixed-methods approach to analyse them towards 1) requirements for a dynamic method, and 2) their efficiency, validity, and complexity. Our results show that the best method to be further developed for dynamic context is Adaptive Self-Explication slightly preferable over Pre-Sorted Self-Explication. Our assessment implicates that an extension of the Adaptive Self-Explication will enable efficient dynamic decision support.