This book is an outgrowth of formal graduate courses in multiple-criteria decision making (MCDM) that the author has taught at the University of Rochester, University of Texas at Austin, and University of Kansas since 1972. The purpose is, on one hand, to offer the reader an integral and systematic view of various concepts and techniques in MCDM at an "introductory" level, and, on the other hand, to provide a basic conception of the human decision mechanism, which may improve our ability to apply the techniques we have learned and may broaden our llJ.ind for modeling human decision making. The book is written with a goal in mind that the reader should be able to assimilate and benefit from most of the concepts in the book if he has the mathematical maturity equivalent to a course in operations research or optimiz ation theory. Good training in linear and nonlinear programming is sufficient to digest, perhaps easily, most of the concepts in the book.

Autorentext

Moussa Larbani is a professor at the International Islamic University Malaysia (IIUM). He earned his bachelor's degree in mathematics, majoring in operations research from USTHB University, Algiers, Algeria in 1985, and his Ph.D. in ordinary differential equations and optimal control from Odessa State University, Ukraine in 1991. He has been Associate Professor at UMMTO University, Tizi-Ouzou, Algeria from 1991-2001. He then joined IIUM University in Kuala Lumpur, Malaysia until 2006. He spent one year at Kainan University in Taiwan and re-joined IIUM University in Kuala Lumpur, Malaysia in 2007. He has been a Professor at IIUM University since 2010. His current research interests include decision making, Habitual Domain theory, fuzzy game theory, supply chain management and multiple criteria problems involving uncertainty. Dr. Larbani has made a significant contribution to the theory of second order games and the theory of decision making in changeable spaces with Prof. P.L. Yu. Moreover, he has published more than 50 articles over multiple objective programming, fuzzy games, differential equations, supply chain management, data mining and finance. Po-Lung Yu, Distinguished Professor (Emeritus) of University of Kansas (KU), Kansas, and Distinguished Professor for Life of National Chiao-Tung University (NCTU), Taiwan, was raised in Taiwan, further educated and trained in USA. He earned BA-International Trade (1963) from National Taiwan University, and Ph.D.-Operations Research and Industrial Engineering (1969) from the Johns Hopkins University. From 1977 to 2004, Dr. Yu held an endowed Chair as the Carl A. Scupin Distinguished Professor of the University of Kansas. He taught at NCTU from 1999 to 2011. Previously he taught at the University of Rochester (1969-73) and the University of Texas at Austin (1973-77). He won awards for outstanding research and for teaching. Dr. Yu, the initiator of Habitual Domains theory, competence set analysis and second order games, has published, in English and Chinese, 22 books and over 180 professional articles over multiple criteria decision making, mathematical programming, differential games and optimal control theory, and various application problems including investment models, efficient market, marketing, automobile safety and energy policy, corporate acquisition and merger analysis, aside from what he initiated. Dr. Yu is recognized internationally as a remarkable thinker, scholar, teacher and advisor. He has given many keynote addresses around the world, academically and publicly. His audiences of Habitual Domains and related topics, sometimes exceeded thousands of people, include professors, students, corporate executives, ministers, military generals, monks, nuns, house wives, jail prisoners, etc.

Inhalt

1. Introduction.- 1.1. The Needs and Basic Elements.- 1.2. An Overview of the Book.- 1.3. Notation.- 2. Binary Relations.- 2.1. Preference as a Binary Relation.- 2.2. Characteristics of Preferences.- 2.3. Optimality Condition.- 2.4. Further Comments.- Exercises.- 3. Pareto Optimal or Efficient Solutions.- 3.1. Introduction.- 3.2. General Properties of Pareto Optimal Solutions.- 3.3. Conditions for Pareto Optimality in the Outcome Space.- 3.4. Conditions for Pareto Optimality in the Decision Space.- 3.5. Further Comments.- 3.6. Appendix: Generalized Gordon Theorem.- 3.7. Appendix: Optimality Conditions.- Exercises.- 4. Goal Setting and Compromise Solutions.- 4.1. Introduction.- 4.2. Satisficing Solutions.- 4.3. Compromise Solutions.- 4.4. Further Comments.- Exercises.- 5. Value Function.- 5.1. Revealed Preference from a Value Function.- 5.2. Conditions for Value Functions to Exist.- 5.3. Additive and Monotonic Value Functions and Preference Separability.- 5.4. Further Comments.- Exercises.- 6. Some Basic Techniques for Constructing Value Functions.- 6.1. Constructing General Value Functions.- 6.2. Constructing Additive Value Functions.- 6.3. Approximation Method.- 6.4. Further Comments.- 6.5. Appendix: Perron-Frobenius Theorem.- Exercises.- 7. Domination Structures and Nondominated Solutions.- 7.1. Introduction.- 7.2. Domination Structures.- 7.3. Constant Dominated Cone Structures.- 7.4. Local and Global N-Points in Domination Structures.- 7.5. Interactive Approximations for N-Points with Information from Domination Structures.- 7.6. Further Comments.- 7.7. Appendix: A Constructive Proof of Theorem 7.3.- Exercises.- 8. Linear Cases, MC- and MC2-Simplex Methods.- 8.1. N-Points in the Linear Case.- 8.2. MC-Simplex Method and Nex-Points.- 8.3. Generating the Set N from Nex-Points.- 8.4. MC2-Simplex Method and Potential Solutions in Linear Systems.- 8.5. Further Comments.- 8.6. Appendix: Proof of Lemma 8.2.- Exercises.- 9. Behavioral Bases and Habitual Domains of Decision Making.- 9.1. Introduction.- 9.2. Behavioral Bases for Decision Making.- 9.3. Habitual Domains.- 9.4. Some Observations in Social Psychology.- 9.5. Some Applications.- 9.6. Further Comments.- 9.7. Appendix: Existence of Stable Habitual Domains.- Exercises.- 10. Further Topics.- 10.1. Interactive Methods for Maximizing Preference Value Functions.- 10.2. Preference over Uncertain Outcomes.- 10.3. Multicriteria Dynamic Optimization Problems.- 10.4. Second-Order Games.- Exercises.