Multi-Criteria and Multi-Dimensional Analysis in Decisions
Description
A new era is emerging in which a group of quantitative methods featuring characteristics of multidimensional comparative analysis (MCA) and multi-criteria decision-making analysis (MCDA) can be used to automate objective decision-making processes. This book introduces the character of the criteria (desirable, non-desirable, motivating, demotivating, and neutral) to MCDA and MCA methods. It presents the author's own developed methods, the preference vector method (PVM), for solving multi-criteria problems in decision making; and, vector measure construction method (VMCM), which is dedicated to solving typical problems in the field of multidimensional comparative analysis. All methods are explained step by step with relevant examples, primarily in the fields of economics and management.
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Person
Kesra Nermend is Professor and Head of the Department of Decision Support Methods and Cognitive Neuroscience; and President of the Centre for Knowledge and Technology Transfer at the Institute of Management, University of Szczecin (Szczecin, Poland). His scientific interests are related to the use of quantitative methods and IT tools in the analysis of socio-economic processes, with particular emphasis on multi-criteria methods, multidimensional data analysis, cognitive neuroscience techniques in researching social behavior and modeling consumer preference in the process of making business decisions. He has published over 130 publications in Polish and English languages including 20 monographs.
Content
Chapter 1 Introduction.- Chapter 2 Problems of multi-criteria and multidimensionality in decision support.- Part I: Methods of multidimensional comparative analysis.- Chapter 3 Initial data analysis procedure.- Chapter 4 Methods for building aggregate measures.- Part II: Multi-criteria decision support methods.- Chapter 5 Methods based on the outranking relationship.- Chapter 6 Methods based on the utility function.- Chapter 7 Multi-criteria methods using function points.- Chapter 8 Conclusions.