Intelligent Strategies for Meta Multiple Criteria Decision Making

205 Pages·2001·6.6 MB·English

by  Thomas Hanne (auth.)

I ntell igent Strategies for
Meta Multiple Criteria Decision Making
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INTELLIGENT STRATEGIES FOR
META MULTIPLE CRITERIA DECISION
MAKING
THOMAS HANNE
.....
"
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication
Hanne, Thomas.
Intelligent strategies for meta multiple criteria decision making / Thomas Hanne.
p. cm. -- (International series in operations research & management
science; 33)
Includes bibliographical references and index.
ISBN 978-1-4613-5632-5 ISBN 978-1-4615-1595-1 (eBook)
DOI 10.1007/978-1-4615-1595-1
1. Multiple criteria decision making. 1. Title. II. Series.
T57.95 .H36 2000
658.4'03--dc21
00-048765
Copyright © 2001 Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2001
Softcover reprint of the hardcover 1 st edition 2001
AH rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanica1, photo
copying, recording, or otherwise, without the prior written permission of the
publisher, Springer Science+Business Media, LLC
Printed on acid-free paper.
Contents
List of Figures ix
List of Tables xi
Preface xiii
Foreword XVll
1. INTRODUCTION 1
1. MCDM problems 1
2. Solutions of MCDM problems 4
3. Decision processes and the application of MCDM methods 5
4. Concepts of 'correct' decision making in MCDM methods 9
5. Summary and conclusions 14
2. THE META DECISION PROBLEM IN MCDM 15
1. Methodological criticism in MCDM 15
1.1 Criticism on single concepts and methods 15
1.2 The discussion on the descriptive orientation of
MCDM 19
1.3 Foundations by axioms of rational behavior 22
2. The meta decision problem in MCDM 24
2.1 Formulation and foundation of the problem 24
2.2 Criteria for method selection 25
2.2.1 The suitability for a type of problem 25
2.2.2 Criteria based on solution concepts 26
2.2.3 Criteria oriented towards 28
implementation
2.2.4 Criteria based on the specific decision
situation 30
2.3 Scalar and multicriteria meta decision problems 31
2.3.1 Scalar evaluations of MCDM methods 31
2.3.2 Method choice as an MADM problem 32
2.4 The meta decision problem as a problem of method
design 34
2.4.1 Determining the parameters of an MCDM
method 34
vi INTELLIGENT STRATEGIES FOR META MCDM
2.4.2 Formalization of MCDM methods 36
2.4.3 A parameter optimization model 37
2.5 The problem of information acquisition 39
2.5.1 Implicit information 40
2.5.2 Explicit information 41
3. Summary and conclusions 44
3. NEURAL NETWORKS AND EVOLUTIONARY LEARNING
FOR MCDM 47
1. Neural networks and MCDM 47
1.1 Introd uction 47
1.2 The construction of neural networks working as
traditional MCDM methods 49
1.3 Neural networks as an adaptive MCDM method 54
2. Evolutionary learning 55
2.1 Evolutionary algorithms and neural networks 56
2.2 Evolutionary algorithms and MCDM 59
3. Summary and conclusions 61
4. ON THE COMBINATION OF MCDM METHODS 63
1. Introd uction 63
2. Properties of MCDM methods 69
3. Properties of specific MCDM methods 71
4. Properties of neurons and neural networks 73
5. The combination of algorithms 74
6. Neural MCDM networks 75
7. Termination and runtime of the algorithm 76
8. Summary and conclusions 77
5. LOOPS - AN OBJECT ORIENTED DSS FOR SOLVING META
DECISION PROBLEMS 79
1. Preliminary remarks 79
2. Method integration, openness, and object oriented
implementation 80
3. A class concept for LOOPS 84
4. Problem solving and learning from an object oriented
point of view 84
5. MADM methods in LOOPS 87
6. Neural networks in LOOPS 89
7. Neural MCDM networks in LOOPS 90
8. Evolutionary algorithms in LOOPS 91
9. An extended interactive framework 95
10. Summary and conclusions 98
6. EXAMPLES OF THE APPLICATION OF LOOPS 99
1. Some remarks on the application of LOOPS 99
Contents vii
2. The learning of utility functions 100
3. Stock selection 106
4. Stock price prediction and the learning of time series 113
5. Stock analysis and long-term prediction 121
6. Method learning 124
7. Meta learning 127
8. An integrated proposal for the application of LOOPS 131
9. Summary and conclusions 132
7. CRITICAL RESUME AND OUTLOOK 135
References 141
Appendices 162
A- Some basic concepts of MCDM theory 163
1. Relations 163
2. Efficiency concepts and scalarizing theorems 165
3. Utility concepts and other axiomatics 166
B- Some selected MCDM methods 169
1. Simple additive weighting 169
2. Achievement levels 169
3. Reference point approaches 170
4. The outranking method PROMETHEE 171
C- Neural networks 173
1. Introduction to neural networks 173
2. Neural networks for intelligent decision support 178
D- Evolutionary algorithms 181
1. Introduction to evolutionary algorithms 181
2. The generalization of evolutionary algorithms 186
E- List of symbols 189
F - List of abbreviations 193
Index 195
List of Figures
1.1 A simple feedback-free model for decision processes 7
1.2 Algorithmic model of a feedback MCDM decision process 9
1.3 The interactive problem solving loop 13
3.1 Schema of a neuron for simple additive weighting 50
3.2 Schema of a neural network for checking the achieve-
ment levels (conjunctive levels) 51
3.3 Schema of a neural network for calculating the lp
distance to a reference point z* 52
3.4 Schema of a neural network for an outranking ap-
proach (PROMETHEE II) 53
4.1 Example of the combination of different methods
for solving an MCDM problem 65
4.2 Subset relations for an efficiency preserving algorithm 70
5.1 Excerpt from the class hierarchy of LOOPS 85
5.2 The relationship between problem objects and method
objects 85
5.3 The relationships between problems, methods, and
meta methods 87
5.4 An interactive approach for solving the meta de-
cision problems 97
6.1 A neural MCDM network for stock selection problems 112
6.2 Graphical representation of the time series of a
stock price index 114
6.3 The relationships of different objects during meta
learning 129
6.4 Flow chart for a possible procedure for the applic-
ation of LOOPS in the framework of meta decision
support 133
D.1 Basic concept of evolutionary algorithms. 183
List of Tables
6.1 Fitness values for the learning of a quadratic util-
ity function depending on the initialization of the
mutation rates without applying the 1/5 rule 103
6.2 Fitness values for the learning of a quadratic util-
ity function depending on the initialization of the
mutation rates with application of the 1/5 rule 103
6.3 Fitness values for the learning of different utility
functions by different methods 104
6.4 Fitness values of the prediction of a stock market
index (DJIA) using different methods (variant 1) 119
6.5 Fitness values of the prediction of a stock market
index (DJIA) using different methods (variant 2) 120
6.6 Fitness values of the prediction of a stock market
index (DJIA) using different methods (variant 3) 120
6.7 Fitness values of the stock price prediction (Volks-
wagen common shares) using different methods 120
6.8 Fitness values of the stock evaluation (long-term
prediction) using different methods 123
6.9 Fitness values of method learning 126
Preface
"For if calculation is calculation, the decision to calculate is not of the order of
the calculable, and must not be. ... The undecidable remains caught, lodged, at
least as a ghost - but an essential ghost - in every decision, in every event of
decision. "
-Jacques Derrida, Force of Law: The "Mystical Foundation of Authority"
Since the early seventies, multiple criteria decision making research
has developed quite rapidly and has become a main area of research for
dealing with complex decision problems which require the consideration
of multiple objectives or criteria. During the last decades a multitude
of methods has been developed which are able to solve such problems.
While a decision maker some decades ago possibly may have felt quite
helpless being confronted with a multicriteria decision problem, today he
or she possibly feels just the same in the face of the plethora of different
methods.
This study deals centrally with this situation which appears on a sci
entific level as a methodological dispute. The meta decision problem
of method ch"Oice and design is analyzed. Different strategies for sup
porting a decision maker in solving this problem and, thus, the actual
decision problem are elaborated. These approaches slip into the design
of a decision support system.
One of the approaches elaborated in this work utilizes machine learn
ing for the design of an MCDM method in the sense of determining
parameters. This is accomplished by adapting a method to a reference
functionality described by training data. For this adaptation, also'intel
ligent techniques' are applied, namely neural networks as a structure for
approximating functions and evolutionary algorithms as universal learn-