390047 DK PhD-M: Multi Criteria Decision Analysis with Partial Information (2015S)

Summary
Multi-Criteria Decision Analysis (MCDA) consists of formal approaches which consider explicitly multiple criteria in helping individuals or groups explore decisions that matter to them. MCDA suggests a structured way to conduct a process, encompassing the activities of structuring the problem situation, building an evaluation, and applying this model to derive recommendations. In MCDA, translating the preferences and values of the individuals or groups into a formal mathematical model is often difficult. Requiring partial information can then be useful to advance in the decision process in a way that is more comfortable for the actors involved in the analysis. This course focuses on how MCDA can be conducted using partial information, reviewing recent research in this very active area. Two distinct MCDA methods will be addressed in detail, in order to present different methodological approaches that can be extended to other MCDA methods.

Learning outcomes upon completing this course
The student will know the different stages of a decision process and the information to be gathered and organized, recognizing the advantages that an MCDA modelling framework may present.
The student will be capable of applying the additive value function model and the ELECTRE TRI model, understanding the role of the parameters to be elicited.
The student will be able to anticipate difficulties that can potentially hinder the elicitation of information from the actors involved in MCDA.
The student will identify different types of partial information and how such information can be translated to an MCDA model in the form of mathematical constraints.
Given partial information, the student will be able to apply simple decision rules, to obtain robust conclusions using mathematical programming techniques, and to obtain statistics using simulation.
The student will have a holistic vision about the tools to deal with partial information and how they may be useful in the context of a decision process, from the perspective of an analyst.
The student will be aware of and will be able to put into context recent research in this area, potentially building on this research for his or her own research work.
In addition, these learning outcomes contribute to develop several generic skills, namely those of critical reasoning, analysis and synthesis, problem solving, information interpretation and management, and group interaction.