Group Verbal Decision Analysis: Theory and Applications

Description

This book describes an original approach to solving tasks of individual and collective choice: classification, ranking, and selection of multi-attribute objects. Object representation with multisets allows considering simultaneously numerical and symbolic variables. In group verbal decision analysis, judgments of all participants are taken into account without a compromise between contradictory. Natural language is used to describe problems and objects, formalize knowledge of experts and preferences of decision makers, and explain results. Verbal methods and technologies are more transparent, less laborious for a person, and weakly sensitive to measurement errors. The book also includes examples of applying new tools in real ill-structured high-dimensional choice tasks. It is intended for researchers, managers, consultants, analysts, and developers as well as for teachers and students of applied mathematics, computer science, information processing, engineering, economics, and management. Preface Contents About the Author 1 Basic Concepts of Decision Theory 1.1 Decision and Choice 1.2 Decision Making Process 1.3 Decision Making Task 1.4 Decision Maker Preferences 1.5 Evaluation of Options 1.6 Comparison of Options 1.7 Choice of Options References 2 Individual and Collective Decisions 2.1 Rationality and Optimality 2.2 Individual Optimal Choice 2.3 Individual Rational Choice 2.4 Verbal Decision Analysis 2.5 Aggregation of Individual Preferences 2.6 Collective Choice 2.7 Group Multicriteria Choice References 3 Group Ordering of Multi-attribute Objects 3.1 Representation and Comparison of Multi-attribute Objects 3.2 Demonstrative Example: Multi-attribute Objects 3.3 Group Ordering of Objects by Multicriteria Pairwise Comparisons: Method RAMPA 3.4 Demonstrative Example: Method RAMPA 3.5 Group Ordering of Objects by Proximity to Reference Points: Method ARAMIS 3.6 Demonstrative Example: Method ARAMIS References 4 Group Classification of Multi-attribute Objects 4.1 Group, without Teachers, Classification of Objects by Feature Proximity: Methods CLAVA-HI and CLAVA-NI 4.2 Demonstrative Example: Method CLAVA-HI 4.3 Group, with Teachers, Classification of Objects by Aggregated Decision Rules: Method MASKA 4.4 Demonstrative Example: Method MASKA References 5 Reducing Dimensionality of Attribute Space 5.1 Hierarchical Structuring Criteria and Attributes: Method HISCRA 5.2 Demonstrative Example: Method HISCRA 5.3 Hierarchical Structuring Criteria and Attributes: Modified Method HISCRA-M 5.4 Demonstrative Example: Method HISCRA-M 5.5 Shortening Criteria and Attributes: Method SOCRATES 5.6 Demonstrative Example: Method SOCRATES References 6 Multicriteria Choice in Attribute Space of High Dimensionality 6.1 Progressive Aggregation of Classified States: Technology PAKS 6.2 Demonstrative Example: Technology PAKS 6.3 Progressive Aggregation of Classified Situations with Many Methods: Technology PAKS-M 6.4 Demonstrative Example: Technology PAKS-M References 7 Practical Applications of Choice Methods 7.1 Analysis of Science Policy Options 7.2 Evaluation of Topicality and Priority of Scientific Directions and Problems 7.3 Formation of Scientific and Technological Program 7.4 Project Competition in Scientific Foundation References 8 Practical Applications of Choice Technologies 8.1 Assessment of Research Results 8.2 Selection of Prospective Computing Complex 8.3 Evaluation of Organization Activity Effectiveness References 9 Mathematical Tools 9.1 Concept of Multiset 9.2 Operations on Multisets 9.3 Families of Sets and Multisets 9.4 Graphical Representations of Multisets 9.5 Set Measure and Multiset Measure 9.6 Metric Spaces of Multisets References Conclusion