Multiple Criteria Decision Analysis in Regional Planning

This book is devoted to presenting theoretical fundamentals for the methods of multiple criteria decision making (MCDM) in the social sciences with particular intent to their applicability to real-world decision making. The main characteristics of the complex problems facing humans in the world today are multidimensional and have multiple objecti ves; they are large-scale, and have nonconimensura te and conflicting objectives, such as economic, environmental, societal, technical, and aesthetic ones. The authors intend to establish basic concepts for treating these complex problems and to present methodological discussions for MCDM with some applications to administrative, or regional, planning. MCDM is composed of two phases: analytical and judgmental. In this book, we intend to consolidate these two phases and to present integrated methodologies for manipulating them with particular interest in managerial decision making, which has not yet been properly treated in spite of its urgent necessi ty. Al though a number of books in MCDM fields have already been published in recent years, most of them have mainly trea ted one aspect of MCDM. Our work specifically intends to trea t the methodology in unified systems and to construct a conceptual structure with special regards to the intrinsic properties of MCDM and its "economic meanings" from the social scientific point of view.

Contenu

1 Introduction to Multiple Criteria Decision Making.- 1.1 Modern Society as a Problems Complex.- 1.2 General Characteristics of Modern Systems Analysis.- 1.2.1 Historical Background.- 1.2.2 Characteristics of Modern Systems Analysis.- 1.3 Characteristics of Multiple Criteria Decision Analysis.- 1.3.1 Objects to be Analyzed.- 1.3.2 General Description of Methodology.- 1.4 Outline of the Book.- 2 Approach to Multiple Criteria Optimization.- 2.1 Pareto Optimality.- 2.1.1 Multiple Criteria Optimization.- 2.1.2 Concept of Pareto Optimality.- 2.1.3 Economic Analysis and Pareto Optimality.- 2.2 Derivation of the Pareto Optimal Frontier.- 2.2.1 Scalarization.- 2.2.2 Kuhn-Tucker Theorems.- 2.2.3 Weighting Method.- 2.2.4 Lagrangian Constraint Method.- 2.2.5 ?-constraint Method.- 2.3 Selection of the Preferred Decisions.- 2.3.1 Search on the Pareto Optimal Frontier.- 2.3.2 Optimal Weights and Market Prices.- 2.3.3 Lagrange Multiplier and Imputed Price.- 2.3.4 Surrogate Worth Trade-off Method.- 3 Interactive Multiobjective Mathematical Programming.- 3.1 Goal Programming and Compromise Programming.- 3.1.1 Goal Programming.- 3.1.2 Compromise Programming.- 3.2 Interactive Frank-Wolfe Method and Its Variants.- 3.2.1 Interactive Frank-Wolfe Method.- 3.2.2 Proxy Method.- 3.2.3 Trade-off Cut Method.- 3.3 Sequential Proxy Optimization Technique (SPOT).- 3.3.1 Introduction.- 3.3.2 Methodological Description.- 3.4 Reference Point Method and Its Extensions.- 3.4.1 Reference Point Method.- 3.4.2 Reference Point Method with Trade-off Information.- 4 Interactive Fuzzy Multiobjective Programming.- 4.1 Introduction.- 4.1.1 Fundamentals of Fuzzy Set Theory.- 4.1.2 Problem Formulation.- 4.2 Interactive Fuzzy Multiobjective Programming without Trade-offs.- 4.2.1 Interactive Fuzzy Goal Programming.- 4.2.2 Interactive Fuzzy Penalty Scalarizing Method.- 4.3 Interactive Fuzzy Multiobjective Programming with Trade-offs.- 4.3.1 Interactive Fuzzy Constraint Method.- 4.3.2 Interactive Fuzzy Minimax Method.- 4.3.3 Interactive Fuzzy Augmented Minimax Method.- 4.4 Interactive Computer Program.- 4.4.1 Computer Package.- 4.4.2 Illustrative Example.- 5 The Preference Structure of Decision Making.- 5.1 Hypothesis of Rational Human Behavior.- 5.1.1 Introduction.- 5.1.2 Axioms for Rational Human Behavior.- 5.2 Expected Utility Hypothesis.- 5.2.1 Preference Relation and Preference Order.- 5.2.2 Existence of the Numerical Utility Function.- 5.2.3 Expected Utility Hypothesis.- 5.2.4 Digressions.- 5.3 The Generalized Nonlinear Utility Function and Risk Attitudes.- 5.4 Application to Technology Assessment for Substitute Energy.- 6 Multiattribute Utility Analysis.- 6.1 Representation Theorem of Multiattribute Utility Functions.- 6.2 Identification of the Single Attribute Utility Function.- 6.3 Identification of the Multiattribute Utility Function.- 6.3.1 Independence Check.- 6.3.2 Assessment of Scaling Constants.- 6.3.3 Checks for the Representation Forms and for Coherence of the Preference Order.- 6.4 Hierarchical Structuring of Preferences: Nesting.- 6.4.1 Decision Hierarchy.- 6.4.2 Nesting of Preferences.- 6.5 Interactive Computer Program for Subjective Systems.- 6.5.1 Discussion on Multiattribute Utility Analysis.- 6.5.2 Assistance by Computer Programs.- 6.5.3 Assessment of the Scaling Constants for MUF.- 6.5.4 The Computer Package: ICOPSS.- 6.5.5 Application to Prior Assessment for the Bullet Train Network.- 7 Value Conflicts in Multiple Agents Decision Making.- 7.1 Extension of Multiattribute Utility Analysis.- 7.2 Approaches to Collective Choice.- 7.2.1 Construction of Group Utility Functions.- 7.2.2 Treatment of the Diversified Evaluation.- 7.3 Probability Assessment and Entropy Model.- 7.3.1 Probability Assessment and Collective Choice.- 7.3.2 Entropy Model and Default Index: The Entropy Evaluation Method (EEM).- 7.3.3 Application to Regional Planning.- 7.4 Fuzzy Multiattribute Utility Analysis.- 7.4.1 Fuzzy Extension of Multiattribute Utility Analysis.- 7.4.2 Fuzzy Preference Ordering.- 7.4.3 Derivation of a Nonfuzzy Collective Preference Ordering.- 7.4.4 Construction of the Fuzzy Multiattribute Utility Function.- 7.5 Stochastic Dominance Rules for Collective Choice.- 8 Reconsideration of Preference Structure.- 8.1 Strength of Preference and Measurable Value Functions.- 8.1.1 Beyond the Concept of the Utility Function under Uncertainty.- 8.1.2 Concepts of the Value Function under Certainty.- 8.1.3 Aggregation Rule of the Measurable Value Function.- 8.2 The Measurable Value Function for Risky Choice.- 8.2.1 Measurable Value Function under Uncertainty.- 8.2.2 Gambles Embodied Preference Differences.- 8.2.3 Foundations of the Expected Value Function.- 8.2.4 Relative Risk Attitude.- 8.2.5 Extensions to Collective Choice.- 8.3 Partial Comparable Axioms without Transitivity.- 8.3.1 Value Function with Indifference Intransitiveness.- 8.3.2 Large Preference and Indistinctive Outranking.- 8.3.3 Application to Organizational Decision Making.- 9 Resource Allocation and Duality.- 9.1 Introduction.- 9.2 Economic Analysis and Equilibrium Prices.- 9.3 Shadow Market and Imputed Prices.- 9.3.1 Negative Utility and Shadow Prices.- 9.3.2 Derivation of the Negative Demand Function.- 9.3.3 Evaluation of the Negative Utility Goods.- 10 Imputation of Dual Prices.- 10.1 Introduction.- 10.2 Nonlinear Programming and Dual Prices.- 10.2.1 Duality of Convex Programming and Its Implication.- 10.2.2 Generalized MUltiobjective Nonlinear Programming and the Dual Prices.- 10.3 The Kuhn-Tucker MUltiplier as General Evaluation Factor.- 10.4 Nested Lagrangian Multiplier (NLM) Method.- 10.4.1 Introduction.- 10.4.2 MUltiobjective Interpretation of Mathematical Programming.- 10.4.3 The Kuhn-Tucker Multiplier as the Basic Evaluation Factor.- 10.4.4 Transformation of the Kuhn-Tucker MUltiplier to a Quasi-Utility Function and Its Nesting.- 10.4.5 Summary and Remarks.- 11 Applications of the Nested Lagrangian Multiplier (NLM) Method.- 11.1 The Nested Lagrangian Multiplier (NLM) Method in Application.- 11.2 MUltiobjective Evaluation of Regional Planning in the Greater Osaka Area: A Static Case.- 11.3 MUltiobjective Evaluation of Regional Planning in the Grea ter Osaka Area: A Dynamic Case.- 11.4 Industrial Land-use Program Combined with Water Quality Control: A Dynamic Case.- 12 Interpretation of Duality in Game Theory.- 12.1 Introduction.- 12.2 Noncooperative Games (Two-person Zerosum).- 12.3 N-Person Cooperative Games and Nucleolus.- 12.4 Example: Evaluation for Efficient Formation of Interregional Agreement.- 13 Propriety of Multiple Criteri…