Decision-making process: concepts and methods
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| Language: | English |
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London [u.a.]
ISTE [u.a.]
2009
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| Item Description: | Includes bibliographical references and index. - Enth. 20 Beitr. |
| Physical Description: | XXXIII, 868 S. graph. Darst. |
| ISBN: | 9781848211162 |
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| 245 | 1 | 0 | |a Decision-making process |b concepts and methods |c ed. by Denis Bouyssou ... |
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| adam_text | DECISION-MAKING PROCESS CONCEPTS AND METHODS EDITED BY DENIS BOUYSSOU
DIDIERDUBOIS MARC PIRLOT HENRI PRADE WILEY CONTENTS PREFACE XXI CHAPTER
1. FROM DECISION THEORY TO DECISION-AIDING METHODOLOGY 1 ALEXIS TSOUKIAS
1.1. INTRODUCTION 1 1.2. HISTORY 4 1.2.1. GENESIS AND YOUTH 4 1.2.2.
MATURITY 10 1.3. DIFFERENT DECISION-AIDING APPROACHES*: 16 1.4. THE
DECISION-AIDING PROCESS 20 1.4.1. THE PROBLEM SITUATION 21 1.4.2. THE
PROBLEM FORMULATION . 22 1.4.3. THE EVALUATION MODEL 23 1.4.4. THE FINAL
RECOMMENDATION .,/ 24 1.5. CONCLUSION 25 1.6. ACKNOWLEDGEMENTS 26 1.7.
BIBLIOGRAPHY 26 CHAPTER 2. BINARY RELATIONS AND PREFERENCE MODELING 49
DENIS BOUYSSOU, PHILIPPE VINCKE 2.1. INTRODUCTION 49 2.2. BINARY
RELATIONS 50 2.2.1. DEFINITIONS 50 2.2.2. PROPERTIES OF A BINARY
RELATION 52 2.2.3. GRAPHICAL REPRESENTATION OF A BINARY RELATION 53
2.2.4. MATRIX REPRESENTATION OF A BINARY RELATION 53 2.2.5. EXAMPLE 53
2.3. BINARY RELATIONS AND PREFERENCE STRUCTURES 54 2.4. CLASSICAL
PREFERENCE STRUCTURES 57 VI DECISION-MAKING PROCESS ; 2.4.1. TOTAL ORDER
57 2.4.1.1. DEFINITION 57 2.4.1.2. NUMERICAL REPRESENTATION 59 . 2.4.2.
WEAK ORDERS 60 2.4.2.1. DEFINITION 60 2.4.2.2. NUMERICAL REPRESENTATION
61 2.4.3. CLASSICAL PROBLEMS 62 2.4.3.1. CHOOSING ON THE BASIS OF BINARY
RELATION 62 2.4.3.2. AGGREGATING PREFERENCES 63 2.4.3.3. PARTICULAR
STRUCTURE OF THE SET OF OBJECTS 63 2.5. SEMI-ORDERS AND INTERVAL ORDERS
66 2.5.1. SEMI-ORDER 66 2.5.1.1. DEFINITION 66 2.5.1.2. WEAK ORDER
ASSOCIATED WITH A SEMI-ORDER 67 2.5.1.3. MATRIX REPRESENTATION 68
2.5.1.4. NUMERICAL REPRESENTATION 68 2.5.2. INTERVAL ORDER 69 2.5.2.1.
DEFINITION 69 2.5.2.2. WEAK ORDERS ASSOCIATED WITH AN INTERVAL ORDER 70
2.5.2.3. MATRIX REPRESENTATION 71 2.5.2.4. NUMERICAL REPRESENTATION 71
2.5.3. REMARKS 72 2.6. PREFERENCE STRUCTURES WITH INCOMPARABILITY 73
2.6.1. PARTIAL ORDER 73 2.6.2. QUASI-ORDER 74 2.6.3. SYNTHESIS 76 2.7.
CONCLUSION 76 2.7.1. OTHER PREFERENCE STRUCTURES 76 2.7.2. OTHER
PROBLEMS 77 2.8. BIBLIOGRAPHY 78 CHAPTER 3. FORMAL REPRESENTATIONS OF
UNCERTAINTY 85 DIDIER DUBOIS, HENRI PRADE 3.1. INTRODUCTION 85 3.2.
INFORMATION: A TYPOLOGY OF DEFECTS 88 3.2.1. INCOMPLETENESS AND
IMPRECISION 89 3.2.2. UNCERTAINTY 91 3.2.3. GRADUAL LINGUISTIC
INFORMATION 94 3.2.4. GRANULARITY 96 3.3. PROBABILITY THEORY 98 3.3.1.
FREQUENTISTS AND SUBJECTIVISTS 98 3.3.2. CONDITIONAL PROBABILITY 101
3.3.3. THE UNIQUE PROBABILITY ASSUMPTION IN THE SUBJECTIVE SETTING ....
104 CONTENTS VII 3.4. INCOMPLETENESS-TOLERANT NUMERICAL UNCERTAINTY
THEORIES 107 3.4.1. IMPRECISE PROBABILITIES 108 3.4.2. RANDOM
DISJUNCTIVE SETS AND BELIEF FUNCTIONS 112 3.4.3. QUANTITATIVE
POSSIBILITY THEORY 118 3.4.3.1. POSSIBILITY THEORY AND BELIEF FUNCTIONS
119 3.4.3.2. POSSIBILITY THEORY AND IMPRECISE PROBABILITIES 119 3.4.3.3.
CLOUDS AND GENERALIZEDP-BOXES 121 3.4.3.4. POSSIBILITY-PROBABILITY
TRANSFORMATIONS 122 3.4.4. POSSIBILITY THEORY AND NON-BAYESIAN
STATISTICS 126 3.5. QUALITATIVE UNCERTAINTY REPRESENTATIONS 127 3.6.
CONDITIONING IN NON-ADDITIVE REPRESENTATIONS 130 3.6.1. CONDITIONAL
EVENTS AND QUALITATIVE CONDITIONING 132 3.6.2. CONDITIONING FOR BELIEF
FUNCTIONS AND IMPRECISE PROBABILITIES ... 134 3.7. FUSION OF IMPRECISE
AND UNCERTAIN INFORMATION 138 3.7.1. NON-BAYESIAN PROBABILISTIC FUSION
140 3.7.2. BAYESIAN PROBABILISTIC FUSION 141 3.7.3. FUSION IN
POSSIBILITY THEORY 142 3.7.4. FUSION OF BELIEF FUNCTIONS 144 3.7.5.
MERGING IMPRECISE PROBABILITY FAMILIES 146 3.8. CONCLUSION 147 3.9.
ACKNOWLEDGEMENTS 147 3.10. BIBLIOGRAPHY 147 CHAPTER 4. HUMAN DECISION:
RECOGNITION PLUS REASONING 157 JEAN-CHARLES POMEROL 4.1. INTRODUCTION:
THE NEUROBIOLOGY OF DECISION, REASONING AND/OR RECOGNITION 157 4.2.
PROCEDURAL RATIONALITY AND LIMITED RATIONALITY 159 4.2.1. SAVAGE S
EXPECTED UTILITY MODEL 159 4.2.2. CHALLENGING UTILITY EXPECTATION 162
4.2.3. BOUNDED RATIONALITY 164 4.2.4. MULTICRITERION DECISION 169 4.2.5.
OTHER MODELS 170 4.3. DECISION BASED ON RECOGNITION 172 4.3.1. DIAGNOSIS
AND DECISION 172 4.3.2. CASE-BASED DECISION 173 4.4. RECOGNITION,
REASONING AND DECISION SUPPORT 175 4.4.1. INTERACTIVE DECISION SUPPORT
SYSTEMS 175 4.4.2. SCENARIOS 176 4.5. COGNITIVE BIASES 178 4.5.1. BIASES
LINKED TO PROBABILITIES 180 4.5.2. REPRESENTATIONS, LEVELS OF
SATISFACTION AND THE ANCHOR EFFECT . ... 182 4.6. CONCLUSION 185 4.7.
ACKNOWLEDGEMENTS 187 4.8. BIBLIOGRAPHY 187 VIII DECISION-MAKING PROCESS
CHAPTER 5. MULTIPLE OBJECTIVE LINEAR PROGRAMMING 199 JACQUES TEGHEM 5.1.
INTRODUCTION 199 5.2. BASIC CONCEPTS AND MAIN RESOLUTION APPROACHES 201
5.2.1. THE PROBLEM 201 5.2.2. DOMINANCE RELATION AND EFFICIENT SOLUTIONS
203 5.2.3. IDEAL POINT, PAYOFF MATRIX AND NADIR POINT 205 5.2.4.
SCALARIZING FUNCTIONS 206 5.2.5. THEOREMS TO CHARACTERIZE EFFICIENT
SOLUTIONS 208 5.2.6. THE MAIN RESOLUTION APPROACHES 209 5.2.6.1. A
PRIORI PREFERENCES 210 5.2.6.2. A POSTERIORI PREFERENCES 213 5.2.6.3.
PROGRESSIVE PREFERENCES OR INTERACTIVE APPROACH 213 5.3. INTERACTIVE
METHODS 215 5.3.1. THE STEP METHOD 215 5.3.1.1. INITIALIZATION (TO = 0)
215 5.3.1.2. GENERAL ITERATION (TO 1) 217 5.3.2. THE STEUER AND CHOO
METHOD 219 5.3.2.1. INITIALIZATION (M = 0) 220 5.3.2.2. GENERAL
ITERATION (TO 1) 220 5.3.3. INTERACTIVE METHODS BASED ON A UTILITY
FUNCTION 222 5.3.3.1. PRINCIPLE OF THE ZIONTS AND WALLENIUS METHOD 222
5.3.3.2. PRINCIPLE OF THE GEOFFRIONEF AL. METHOD 223 5.4. THE MULTIPLE
OBJECTIVE INTEGER PROGRAMMING 224 5.4.1. METHODS OF GENERATING E(P) 226
5.4.1.1. THE KLEIN AND HANNAN METHOD 226 5.4.1.2. THE SYLVA AND CREMA
METHOD 226 5.4.1.3. THE KIZILTAN AND YUCAOGLU METHOD 227 5.4.2.
INTERACTIVE METHODS 228 5.4.2.1. GONZALESEFAZ. METHOD 228 5.4.2.2. THE
MOMIX METHOD 229 5.5. THE MULTIPLE OBJECTIVE COMBINATORIAL OPTIMIZATION
231 5.5.1. EXACT METHODS 233 5.5.1.1. DIRECT METHODS 235 5.5.1.2. THE
TWO PHASES METHOD 237 5.5.1.3. COMMENTS 240 5.5.2. METAHEURISTICS 241
5.5.2.1. SIMULATED ANNEALING . 241 5.5.2.2. TABU SEARCH 243 5.5.2.3.
GENETIC ALGORITHMS 243 5.6. THE MULTIPLE OBJECTIVE STOCHASTIC LINEAR
PROGRAMMING 245 5.6.1. THE EQUIVALENT DETERMINISTIC PROBLEM 247 5.6.2.
DETERMINATION OF THE FIRST COMPROMISE 248 CONTENTS IX 5.6.2.1. PAYOFF
MATRIX 248 5.6.2.2. WEIGHTS ASSOCIATED WITH THE OBJECTIVES 249 5.6.2.3.
FIRST COMPROMISE 249 5.6.3. INTERACTIVE PHASES 249 5.6.3.1. INFORMATION
GIVEN TO THE DECISION MAKER 249 5.6.3.2. FIRST INTERACTION WITH THE
DECISION MAKER 250 5.6.3.3. COMPUTATIONAL PHASE 250 5.7. THE MULTIPLE
OBJECTIVE FUZZY LINEAR PROGRAMMING 253 5.7.1. COMPARISON OF TWO FUZZY
NUMBERS 253 5.7.1.1. AREA COMPENSATION 254 5.7.1.2. DETERMINATION OF I
(A ) 256 5.7.1.3. EQUIVALENT CRISP CONSTRAINT 257 5.7.2. TREATMENT OF
A FUZZY OBJECTIVE FUNCTION 257 5.7.3. THE CRISP (DETERMINISTIC)
EQUIVALENT PROBLEM 258 5.8. CONCLUSION 258 5.9. BIBLIOGRAPHY 258 CHAPTER
6. CONSTRAINT SATISFACTION PROBLEMS 265 GERARD VERFAILLIE, THOMAS SCHIEX
6.1. INTRODUCTION 265 6.2. THE CSP FRAMEWORK 266 6.2.1. SYNTACTICAL PART
.__. 267 6.2.2. SEMANTICAL PART . . 268 6.2.3. ASSIGNMENTS 269 6.2.4.
QUERIES 269 6.3. COMPLEXITY 271 6.4. RELATED PROBLEMS - 272 6.5.
REASONING ON A CSP 274 6.5.1. LOCAL CONSISTENCY 275 6.5.2.
ARC-CONSISTENCY 275 6.5.3. PATH-CONSISTENCY 278 6.5.4. OTHER LOCAL
CONSISTENCY PROPERTIES 280 6.5.5. GENERAL CONSTRAINT PROPAGATION
MECHANISMS 282 6.6. LOOKING FOR A CSP SOLUTION 283 6.6.1. TREE SEARCH
283 6.6.2. VARIABLE ELIMINATION 288 6.6.3. GREEDY SEARCH 291 6.6.4.
LOCAL SEARCH 292 6.7. EXPERIMENTAL EVALUATIONS AND LESSONS LEARNED 294
6.7.1. PURE CONSTRAINT SATISFACTION PROBLEMS 294 6.7.2. CONSTRAINT
OPTIMIZATION PROBLEMS 294 6.8. POLYNOMIAL CLASSES 295 6.8.1. ACYCLIC
CONSTRAINT NETWORKS 296 X DECISION-MAKING PROCESS , 6.8.2. SIMPLE
TEMPORAL CONSTRAINT NETWORKS 297 6.9. EXISTING TOOLS 297 6.10.
EXTENSIONS OF THE BASIC FRAMEWORK 298 6.10.1. CONTINUOUS DOMAINS 298
6.10.2. CONDITIONAL PROBLEMS 300 6.10.3. DYNAMIC PROBLEMS 301 6.10.4.
CONSTRAINTS AND PREFERENCES 301 6.11. OPEN PROBLEMS 308 6.11.1.
CONSTRAINTS AND UNCERTAINTIES 308 6.11.2. DECIDING OR REASONING UNDER
TIME CONSTRAINTS 309 6.11.3. INTERACTIVE DECISION 310 6.11.4.
DISTRIBUTED DECISION 310 6.12. BOOKS, JOURNALS, WEBSITES AND CONFERENCES
311 6.13. BIBLIOGRAPHY 311 CHAPTER 7. LOGICAL REPRESENTATION OF
PREFERENCES 321 JEROME LANG 7.1. INTRODUCTION 321 7.2. BASICS OF
PROPOSITIONAL LOGIC 324 7.3. PRINCIPLES AND ELEMENTARY LANGUAGES 326
7.4. WEIGHTS, PRIORITIES AND DISTANCES 329 7.4.1. WEIGHTS ._ _ 329
7.4.1.1. BIBLIOGRAPHICAL NOTES . . 7 332 7.4.2. PRIORITIES 332 7.4.2.1.
BEST-OUT 333 7.4.2.2. DISCRIMIN 333 7.4.2.3. LEXIMIN 334 7.4.2.4.
BIBLIOGRAPHICAL NOTES . 334 7.4.3. DISTANCES 335 7.4.3.1.
BIBLIOGRAPHICAL NOTES 337 7.5. PREFERENCE LOGICS: CONDITIONALS AND
CETERIS PARIBUS PREFERENCES 338 7.5.1. CETERIS PARIBUS PREFERENCES 338
7.5.1.1. PREFERENCES BETWEEN NON-CONTRADICTORY FORMULAE 339 7.5.1.2.
CETERIS PARIBUS COMPARISONS AND THEIR GENERALIZATIONS . . . . 340
7.5.1.3. PREFERENCE RELATION INDUCED BY CETERIS PARIBUS PREFERENCES .
342 7.5.1.4. CP-NETS 343 7.5.1.5. COMMENTS AND BIBLIOGRAPHICAL NOTES 345
7.5.2. DEFEASIBLE PREFERENCES AND CONDITIONAL PREFERENCE LOGICS 345
7.5.2.1. BIBLIOGRAPHICAL NOTES 351 7.5.3. LOGICAL MODELING OF INCOMPLETE
AND/OR CONTRADICTORY PREFERENCES 352 7.6. DISCUSSION 353 7.6.1.
COGNITIVE AND LINGUISTIC RELEVANCE, ELICITATION 353 7.6.2. EXPRESSIVITY
353 CONTENTS XI 7.6.3. COMPLEXITY AND ALGORITHMS 354 7.6.4. SPATIAL
EFFICIENCY 354 7.7. ACKNOWLEDGEMENTS 355 7.8. BIBLIOGRAPHY 355 CHAPTER
8. DECISION UNDER RISK: THE CLASSICAL EXPECTED UTILITY MODEL . . 365
ALAIN CHATEAUNEUF, MICHELE COHEN, JEAN-MARC TALLON 8.1. INTRODUCTION 365
8.1.1. DECISION UNDER UNCERTAINTY 366 8.1.2. RISK VERSUS UNCERTAINTY 366
8.2. RISK AND INCREASING RISK: COMPARISON AND MEASURES 367 8.2.1.
NOTATION AND DEFINITIONS 367 8.2.1.1. FIRST-ORDER STOCHASTIC DOMINANCE
368 8.2.1.2. SECOND-ORDER STOCHASTIC DOMINANCE 369 8.2.2. BEHAVIOR UNDER
RISK 370 8.2.2.1. MODEL-FREE BEHAVIORAL DEFINITIONS 370 8.2.2.2.
CERTAINTY EQUIVALENT, RISK PREMIUM AND BEHAVIOR COMPARISON 371 8.3.
EXPECTED UTILITY (EU) MODEL 372 8.3.1. MIXING PROBABILITY DISTRIBUTIONS
372 8.3.2. GENERALIZED MIXTURE 373 8.3.3. AXIOMATIC FOUNDATION OF THE EU
MODEL 373 8.3.3.1. LINEAR UTILITY THEOREM 374 8.3.3.2. VON
NEUMANN-MORGENSTERN THEOREM FOR DISTRIBUTIONS WITH FINITE SUPPORT IN
(C,G) 375 8.3.3.3. VON NEUMANN-MORGENSTERN THEOREM FOR DISTRIBUTIONS
WITH BOUNDED SUPORT IN (C,Q) . 376 8.3.3.4. VON NEUMANN-MORGENSTERN
THEOREM FOR DISTRIBUTIONS WITH BOUNDED SUPPORT IN (M, B) 376 8.3.4.
CHARACTERIZATION OF RISK AVERSION IN THE EU MODEL 377 8.3.4.1.
CHARACTERIZATION OF FIRST- AND SECOND-ORDER DOMINANCE IN THE EU MODEL
377 8.3.5. COEFFICIENT OF ABSOLUTE RISK AVERSION, LOCAL VALUE OF THE
RISK PREMIUM 378 8.3.5.1. COEFFICIENT OF ABSOLUTE RISK AVERSION 378
8.3.5.2. LOCAL VALUE OF THE RISK PREMIUM 378 8.3.5.3. VARIANCE AND EU
MODEL 379 8.4. PROBLEMS RAISED BY THE EU MODEL 379 8.4.1. ALLAIS PARADOX
379 8.4.2. INTERPRETING THE UTILITY FUNCTION 380 8.4.3. WEAK AND STRONG
RISK AVERSION UNDER EXPECTED UTILITY 380 8.4.4. NOTION OF SSD AS A RISK
INDICATOR IN THE EU MODEL 381 8.5. SOME ALTERNATIVE MODELS 381 8.5.1.
MACHINA S MODEL 381 8.5.2. MODELS WITH SECURITY AND POTENTIAL LEVELS 382
XII DECISION-MAKING PROCESS 8.6. ACKNOWLEDGEMENTS 382 8.7. BIBLIOGRAPHY
382 CHAPTER 9. DECISION UNDER UNCERTAINTY: THE CLASSICAL MODELS 385
ALAIN CHATEAUNEUF, MICHELE COHEN, JEAN-YVES JAFFRAY 9.1. INTRODUCTION
385 9.2. SUBJECTIVE EXPECTED UTILITY (SEU) 386 9.2.1. DEFINITIONS AND
NOTATION 386 9.2.2. THE SEU CRITERION 386 9.3. SAVAGE S THEORY 387
9.3.1. SAVAGE S AXIOMS AND THEIR INTERPRETATION AND IMPLICATIONS 387
9.3.1.1. PREFERENCES ON THE ACTS 387 9.3.2. CONSTRUCTION OF SAVAGE S
THEORY 391 9.3.2.1. FROM QUALITATIVE TO SUBJECTIVE PROBABILITIES 391
9.3.2.2. SUBJECTIVE LOTTERIES AND LINEAR UTILITY 392 9.3.2.3. EXTENSION
OF SEU TO ALL ACTS 394 9.3.3. THE ELLSBERG PARADOX 394 9.4. ANSCOMBE AND
AUMANN THEORY 396 9.4.1. THE ANSCOMBE-AUMANN AXIOM SYSTEM 396 9.4.2.
COMMENTS AND DISCUSSION 397 9.4.3. THE ANSCOMBE-AUMANN REPRESENTATION
THEOREM 397 9.4.4. RETURN TO THE ELLSBERG PARADOX 398 9.5. CONCLUSION 7
399 9.6. BIBLIOGRAPHY 399 CHAPTER 10. CARDINAL EXTENSIONS OF THE EU
MODEL BASED ON THE CHOQUET INTEGRAL 401 ALAIN CHATEAUNEUF, MICHELE COHEN
10.1. INTRODUCTION 401 10.2. NOTATION AND DEFINITIONS 402 10.2.1. THE
NOTION OF COMONOTONY 403 10.2.2. THE CHOQUET INTEGRAL 404 10.2.3.
CHARACTERIZATION OF THE CHOQUET INTEGRAL 405 10.3. DECISION UNDER
UNCERTAINTY 405 10.3.1. ELLSBERG S PARADOX 405 10.3.1.1. INTERPRETATION
OF ELLSBERG S PARADOX IN THE FRAMEWORK OF SAVAGE 406 10.3.1.2.
INTERPRETATION OF ELLSBERG S PARADOX IN THE FRAMEWORK OF ANSCOMBE AND
AUMANN 406 10.3.2. SCHMEIDLER S MODEL IN ANSCOMBE-AUMANN FRAMEWORK 407
10.3.2.1. COMONOTONIC INDEPENDENCE 408 10.3.2.2. REPRESENTATION OF
PREFERENCES BY A CHOQUET INTEGRAL IN ANSCOMBE-AUMANN S FRAMEWORK 408
CONTENTS XIII 10.3.3. CHOQUET EXPECTED UTILITY (CEU) MODELS IN SAVAGE S
FRAMEWORK . 409 10.3.3.1. SIMPLIFIED VERSION OF SCHMEIDLER S MODEL IN
SAVAGE S FRAMEWORK 409 10.3.3.2. CHOQUET EXPECTED UTILITY MODEL IN
SAVAGE S FRAMEWORK . . 411 10.3.3.3. EXAMPLE OF COMPUTATION OF SUCH A
CHOQUET INTEGRAL .... 412 10.3.3.4. THE COMONOTONIC SURE-THING PRINCIPLE
412 10.3.4. UNCERTAINTY AVERSION 413 10.3.5. THE MULTIPRIOR MODEL 415
10.3.5.1. THE AXIOMATIC OF THE MODEL 415 10.3.5.2. COMPARING MULTIPRIOR
MODEL WITH CHOQUET UTILITY MODEL . 416 10.3.5.3. CEU MODEL AND LOWER AND
UPPER ENVELOPES OF A PROBABILITY DISTRIBUTIONS FAMILY 417 10.4. DECISION
UNDER RISK 418 10.4.1. EU MODEL AND ALLAIS PARADOX 419 10.4.2. THE
RANK-DEPENDENT EXPECTED UTILITY MODEL 420 10.4.2.1. DEFINITION OF THE
RANK-DEPENDENT EXPECTED UTILITY MODEL . . 420 10.4.2.2. KEY AXIOM OF
RDU S AXIOMATIZATION: COMONOTONIC SURE- THING PRINCIPLE 422 10.4.3. FROM
THE CEU TO THE RDU MODEL USING FIRST-ORDER STOCHASTIC DOMINANCE 424
10.4.3.1. RDU REPRESENTATION IS A CHOQUET INTEGRAL 424 10.4.3.2. FROM
THE CEU TO THE RDU 424 10.4.4. RISK AVERSION NOTIONS AND
CHARACTERIZATION IN THE RDU MODEL . . 425 10.4.4.1. STRONG RISK AVERSION
426 10.4.4.2. MONOTONE RISK AVERSION 426 10.4.4.3. LEFT MONOTONE RISK
AVERSION 427 10.4.4.4. CHARACTERIZATION OF RISK AVERSION NOTIONS IN THE
RDU MODEL 428 10.5. BIBLIOGRAPHY . 429 CHAPTER 11. A SURVEY OF
QUALITATIVE DECISION RULES UNDER UNCERTAINTY . . 435 DIDIER DUBOIS,
HELENE FARGIER, HENRI PRADE, REGIS SABBADIN 11.1. INTRODUCTION 435 11.2.
QUANTITATIVE VERSUS QUALITATIVE DECISION RULES 437 11.3. ORDINAL
DECISION RULE WITHOUT COMMENSURATENESS 441 11.4. AXIOMATICS OF
QUALITATIVE DECISION THEORY 445 11.4.1. SAVAGE S THEORY: A REFRESHER 445
11.4.2. THE RELATIONAL APPROACH TO DECISION THEORY 449 11.4.3.
QUALITATIVE DECISION RULES UNDER COMMENSURATENESS 452 11.5. TOWARD MORE
EFFICIENT QUALITATIVE DECISION RULES 457 11.5.1. REFINING QUALITATIVE
CRITERIA 458 11.5.2. A BRIDGE BETWEEN GENERALIZED MAXMIN CRITERIA AND
EXPECTED UTILITY 460 11.5.3. WEIGHTED LEXIMAX/LEXIMIN CRITERIA 463 XIV
DECISION-MAKING PROCESS 11.5.4. THE REPRESENTATION OF UNCERTAINTY
UNDERLYING LEXIMAX(LEXIMIN) AND LEXIMIN(LEXIMAX) CRITERIA 465 11.6.
CONCLUSION 466 11.7. BIBLIOGRAPHY 467 CHAPTER 12. A COGNITIVE APPROACH
TO HUMAN DECISION MAKING 475 ERIC RAUFASTE, DENIS J. HILTON 12.1.
INTRODUCTION 475 12.2. HUMANS DO NOT MATCH CURRENT RATIONAL MODELS 476
12.2.1. OVERCONFIDENCE AND CALIBRATION OF JUDGEMENT 476 12.2.2.
PREFERENCE REVERSALS AND FRAMING EFFECTS 477 12.2.3. SUBJECTIVATION OF
EXPECTED UTILITY: PROSPECT THEORY 478 12.2.4. QUESTIONS RAISED BY THE
STANDARD MODEL 480 12.3. A GLOBAL DESCRIPTIVE APPROACH TO DECISION
MAKING 481 12.3.1. THE CONCEPT OF MULTICRITERIA DECISION MAKING 482
12.3.2. THE NOTION OF DOMINANCE STRUCTURE 483 12.3.2.1. THE DOMINANCE
RULE 483 12.3.2.2. THE SEARCH FOR DOMINANCE 483 12.3.2.3. DOMINANCE
STRUCTURES 483 12.3.3. STEPS IN THE DECISION MAKING PROCESS 484
12.3.3.1. PRE-EDITION 484 12.3.3.2. SEARCH FOR A FOCAL ALTERNATIVE 485
12.3.3.3. THE TEST OF DOMINANCE 485 12.3.3.4. DOMINANCE STRUCTURING 486
12.4. ATTENTIONAL FOCUSING 487 12.5. EVALUATION HEURISTICS AND
ECOLOGICAL RATIONALITY 489 12.5.1. LOGICAL RATIONALITY AND ECOLOGICAL
RATIONALITY 489 12.5.2. THE REPRESENTATIVENESS HEURISTIC 491 12.5.3. THE
AVAILABILITY HEURISTIC 492 12.5.4. THE ANCHORING-ADJUSTMENT HEURISTIC
493 12.5.5. CONCLUSION ON HEURISTICS 494 12.6. THE ROLE OF AFFECT IN
DECISION MAKING 495 12.6.1. THE POSITIVE ROLE OF EMOTIONS 495 12.6.2.
AFFECT AND EXPECTED UTILITY 496 12.7. CONCLUSION 498 12.8. BIBLIOGRAPHY
499 CHAPTER 13. BAYESIAN NETWORKS 505 JEAN-YVES JAFFRAY 13.1.
INTRODUCTION 505 13.2. DEFINITIONS AND NOTATION 507 13.2.1. JOINT AND
MARGINAL PROBABILITIES 507 13.2.2. INDEPENDENCE 508 CONTENTS XV 13.2.3.
CONDITIONAL PROBABILITIES 509 13.2.4. CONDITIONAL INDEPENDENCE 510
13.2.5. BAYESIAN NETWORK 511 13.2.6. GRAPHICAL CONDITIONAL INDEPENDENCE
CRITERION IN BNS: D-SEPARATION 513 13.2.6.1. D-SEPARATION 515 13.3.
EVIDENTIAL DATA PROCESSING IN A BN 516 13.3.1. PEARL S METHOD 517
13.3.2. THE JUNCTION TREE METHOD 523 13.3.2.1. CONSTRUCTION OF THE
JUNCTION TREE 523 13.3.2.2. EVIDENTIAL DATA PROCESSING IN A JUNCTION
TREE 525 13.4. CONSTRUCTING A BN 528 13.4.1. SCORE-BASED METHODS 528
13.4.2. CONDITIONAL INDEPENDENCE BASED METHODS 529 13.4.3. SEARCH AMONG
MARKOV EQUIVALENCE CLASSES 529 13.4.4. CAUSALITY 530 13.4.5.
CONDITIONING BY INTERVENTION IN CAUSAL GRAPHS 531 13.5. BNS AND
INFLUENCE DIAGRAMS 532 13.5.1. DYNAMIC DECISION MAKING UNDER UNCERTAINTY
532 13.5.1.1. AN EXAMPLEOF DYNAMIC DECISION PROBLEM UNDERRISK . . . .
533 13.5.1.2. DECISION TREE OF THE PROBLEM 533 13.5.1.3. OPTIMIZATION BY
DYNAMIC PROGRAMMING 534 13.5.1.4. LIMITS OF THE CLASSICAL METHOD 535
13.5.2. INFLUENCE DIAGRAMS ~» 535 13.5.2.1. ORIGIN OF THE INFLUENCE
DIAGRAMS 535 13.5.2.2. SEMANTICS OF IDS 536 13.5.2.3. THE METHODS OF
SHACHTER AND SHENOY 536 13.5.2.4. THE JUNCTION TREE METHOD 537 13.6.
CONCLUSION * . 537 13.7. SOFTWARE 538 13.8. BIBLIOGRAPHY 538 CHAPTER
14. PLANNING UNDER UNCERTAINTY WITH MARKOV DECISION PROCESSES 541 REGIS
SABBADIN 14.1. INTRODUCTION 541 14.2. MARKOV DECISION PROCESSES 542
14.2.1. PROBLEM FORMULATION 542 14.2.1.1. STATES, ACTIONS, TRANSITIONS
AND POLICIES 542 14.2.1.2. REWARD, CRITERION, VALUE FUNCTION, OPTIMAL
POLICY 543 14.2.2. CLASSICAL SOLUTION ALGORITHMS FOR MDP 544 14.2.2.1.
FINITE HORIZON: BACKWARDS INDUCTION 544 14.2.2.2. INFINITE HORIZON:
VALUE ITERATION AND POLICY ITERATION .... 544 14.2.3. EXAMPLE: CAR RACE
546 14.2.4. RECENT ADVANCES IN MARKOV DECISION PROCESSES 548 XVI
DECISION-MAKING PROCESS 14.3. PARTIALLY OBSERVED MDPS 549 14.3.1. POMDP
MODEL, CONTINUOUS-MDP TRANSFORMATION 549 14.3.2. COMPUTING OPTIMAL
POLICIES IN A POMDP 550 14.3.2.1. I-POLICY TREE 551 14.3.2.2. VALUE
ITERATION ALGORITHM FOR POMDP 553 14.3.3. POMDP EXAMPLE 553 14.3.4.
CONCLUDING REMARKS 554 14.4. REAL-TIME DYNAMIC PROGRAMMING AND
REINFORCEMENT LEARNING 555 14:4.1. INTRODUCTION 555 14.4.2. REAL-TIME
DYNAMIC PROGRAMMING 555 14.4.2.1. GAUSS-SEIDEL ALGORITHM 555 14.4.2.2.
ASYNCHRONOUS DYNAMIC PROGRAMMING 556 14.4.2.3. REAL-TIME DYNAMIC
PROGRAMMING 557 14.4.3. REINFORCEMENT LEARNING 558 14.4.3.1. INDIRECT
REINFORCEMENT LEARNING 558 14.4.3.2. DIRECT REINFORCEMENT LEARNING 559
14.4.4. CONCLUDING REMARKS 560 14.5. FACTORED MARKOV DECISION PROCESSES
561 14.5.1. STATE SPACE FACTORIZATION, STATIONARY HOMOGENOUS BAYESIAN
NET- WORKS 561 14.5.2. FACTORED REPRESENTATION OF ACTIONS 563 14.5.3.
FACTORED REPRESENTATION OF REWARDS 563 14.5.4. FACTORED REPRESENTATION
OF VALUE FUNCTIONS AND POLICIES AND COMPUTATION OF OPTIMAL POLICIES 564
14.5.5. CONCLUDING REMARKS 564 14.6. POSSIBILISTIC MARKOV DECISION
PROCESSES 565 14.6.1. BACKGROUND ON QUALITATIVE POSSIBILITY THEORY 565
14.6.2. POSSIBILISTIC COUNTERPARTS OF EXPECTED UTILITY 566 14.6.3.
POSSIBILISTIC MARKOV DECISION PROCESSES 568 14.6.3.1. FINITE HORIZON 568
14.6.3.2. POSSIBILISTIC VALUE ITERATION 570 14.6.3.3. POLICY ITERATION
ALGORITHM 572 14.6.4. CONCLUDING REMARKS 573 14.7. CONCLUSION 573 14.8.
BIBLIOGRAPHY 574 CHAPTER 15. MULTIATTRIBUTE UTILITY THEORY 579 MOHAMMED
ABDELLAOUI, CHRISTOPHE GONZALES 15.1. INTRODUCTION 579 15.2.
INTRODUCTION TO UTILITY THEORY 580 15.2.1. UTILITY FUNCTIONS 580 15.2.2.
DECISION UNDER CERTAINTY, UNCERTAINTY AND RISK 581 15.2.3.
MULTIATTRIBUTE UTILITY FUNCTIONS 583 CONTENTS XVII 15.2.4.
DECOMPOSITIONS OF UTILITY FUNCTIONS 584 15.3. DECOMPOSITION UNDER
CERTAINTY 586 15.3.1. ADDITIVE DECOMPOSITION IN TWO-DIMENSIONAL SPACES
586 15.3.2. EXTENSION TO MORE GENERAL OUTCOME SETS 594 15.4.
DECOMPOSITIONS UNDER UNCERTAINTY 597 15.4.1. DECOMPOSITION IN
TWO-DIMENSIONAL SPACES 599 15.4.2. EXTENSION OF THE TWO-DIMENSIONAL
DECOMPOSITION 603 15.5. ELICITATION OF UTILITY FUNCTIONS 605 15.5.1.
ELICITATION UNDER CERTAINTY 606 15.5.2. ELICITATION UNDER UNCERTAINTY
609 15.6. CONCLUSION 613 15.7. BIBLIOGRAPHY 614 CHAPTER 16. CONJOINT
MEASUREMENT MODELS FOR PREFERENCE RELATIONS ... 617 DENIS BOUYSSOU, MARC
PLRLOT 16.1. INTRODUCTION 617 16.1.1. BRIEF OVERVIEW OF CONJOINT
MEASUREMENT MODELS 618 16.1.2. CHAPTER CONTENTS 620 16.2. FUNDAMENTAL
RELATIONS AND TRIVIAL MODELS 623 16.2.1. BINARY RELATIONS ON A PRODUCT
SET 623 16.2.2. INDEPENDENCE AND MARGINAL PREFERENCES 624 16.2.3.
MARGINAL TRACES ON LEVELS 625 16.2.4. MARGINAL TRACES ON DIFFERENCES 626
16.2.5. THREE MODELS FOR GENERAL RELATIONS ON A CARTESIAN PRODUCT ....
628 16.3. MODELS USING MARGINAL TRACES ON LEVELS 629 16.3.1. DEFINITION
OF THE MODELS 629 16.3.2. COMPLETENESS OF MARGINAL TRACES AND
MONOTONICITY OF F 631 16.3.3. MODEL (L8) AND STRICT MONOTONICITY W.R.T.
TRACES 634 16.3.4. COMPLETE CHARACTERIZATION OF THE MODELS ON LEVELS 636
16.3.4.1. UNIQUENESS AND REGULAR REPRESENTATIONS 637 16.3.5. RELATIONS
COMPATIBLE WITH DOMINANCE 637 16.3.6. STRICT COMPATIBILITY WITH
DOMINANCE 640 16.3.7. THE CASE OF WEAK ORDERS 641 16.3.8. EXAMPLES 642
16.4. MODELS USING MARGINAL TRACES ON DIFFERENCES 644 16.4.1. MODELS
DEFINITION 644 16.4.2. COMPLETENESS OF MARGINAL TRACES ON DIFFERENCES
AND MONOTONICITY OF G 646 16.4.3. CHARACTERIZATION OF MODEL ( 11) 650
16.4.4. REMARKS 651 16.4.4.1. GOLDSTEIN S MODEL 651 16.4.4.2. MARGINAL
PREFERENCES 651 16.4.4.3. UNIQUENESS OF THE REPRESENTATION 652 XVIII
DECISION-MAKING PROCESS * 16.4.5. EXAMPLES 653 16.5. MODELS USING BOTH
MARGINAL TRACES ON LEVELS AND ON DIFFERENCES .... 655 - 16.5.1.
RELATIONSHIPS BETWEEN TRACES ON DIFFERENCES AND ON LEVELS .... 657
16.5.2. STUDY OF MODELS (LIDO) TO (L1D11) AND (L2D0) TO (L2D11) . 661
16.5.3. EXAMPLES 664 16.6. CONCLUSION 665 16.7. BIBLIOGRAPHY 667 CHAPTER
17. AGGREGATION FUNCTIONS FOR DECISION MAKING 673 JEAN-LUC MARICHAL
17.1. INTRODUCTION 673 17.2. AGGREGATION PROPERTIES 676 17.2.1.
ELEMENTARY MATHEMATICAL PROPERTIES 677 17.2.2. STABILITY PROPERTIES
RELATED TO SCALE TYPES 678 17.2.3. ALGEBRAIC PROPERTIES 680 17.3. MEANS
682 17.3.1. QUASI-ARITHMETIC MEANS 685 17.3.2. LAGRANGIAN AND CAUCHY
MEANS 687 17.4. ASSOCIATIVE AGGREGATION FUNCTIONS 688 17.4.1. STRICTLY
INCREASING FUNCTIONS 689 17.4.2. ARCHIMEDEAN SEMIGROUPS 690 17.4.3. A
CLASS OF NON-DECREASING AND ASSOCIATIVE FUNCTIONS 693 17.4.4. INTERNAL
ASSOCIATIVE FUNCTIONS 695 17.4.5. T-NORMS, T-CONORMS, AND UNINORMS 697
17.5. NON-ADDITIVE INTEGRALS 698 17.5.1. MOTIVATIONS 698 17.5.2. THE
CHOQUET INTEGRAL 700 17.5.3. THE SUGENO INTEGRAL 704 17.6. AGGREGATION
ON RATIO AND INTERVAL SCALES 709 17.7. AGGREGATION ON ORDINAL SCALES 711
17.8. CONCLUSION 714 17.9. BIBLIOGRAPHY 714 CHAPTER 18. SUBJECTIVE
EVALUATION 723 MICHEL GRABISCH 18.1. INTRODUCTION 723 18.2. WHAT IS
SUBJECTIVE EVALUATION? 725 18.2.1. GENERAL DEFINITION AND RELATED
DOMAINS 725 18.2.2. DEFINITION OF OUR SCOPE 726 18.3. A MULTICRITERIA
APPROACH TO SUBJECTIVE EVALUATION 727 18.3.1. THE IMPORTANCE OF AFFECT
IN EVALUATION 728 18.3.2. MEASUREMENT THEORY, NOTION OF SCALE 729
18.3.3. UNIPOLAR AND BIPOLAR SCALES 733 CONTENTS XIX 18.3.4. THE MACBETH
APPROACH 735 18.3.5. CONSTRUCTION OF THE MODEL OF SUBJECTIVE EVALUATION
738 18.4. CONSTRUCTION OF ME AGGREGATION FUNCTION 740 18.4.1. CASE OF
CARDINAL UNIPOLAR SCALES 740 18.4.2. CASE OF CARDINAL BIPOLAR SCALES 743
18.5. THE CASE OF ORDINAL SCALES 747 18.5.1. INTRODUCTION 747 18.5.2.
THE SUGENO INTEGRAL 749 18.5.3. THE SYMMETRIC SUGENO INTEGRAL AND
BIPOLAR MODELS 750 18.6. IDENTIFICATION OF THE PARAMETERS OF THE
AGGREGATION FUNCTION 752 18.6.1. CARDINAL CASE 753 18.6.2. ORDINAL CASE
755 18.7. INTERPRETATION OF THE AGGREGATION FUNCTION 758 18.7.1. INDEX
OF IMPORTANCE OF A CRITERION 759 18.7.2. INDEX OF INTERACTION 760
18.7.3. MAXIMUM IMPROVING INDEX 764 18.7.4. CONJUNCTION AND DISJUNCTION
INDICES 765 18.7.5. VETO AND INDEX OF VETO 765 18.8. PARTICULAR FAMILIES
OF CAPACITIES AND BICAPACITIES 766 18.9. APPLICATIONS 768 18.10.
CONCLUSION 771 18.11. BIBLIOGRAPHY 771 CHAPTER 19. SOCIAL CHOICE THEORY
AND MULTICRITERIA DECISION AIDING . . . 779 DENIS BOUYSSOU, THIERRY
MARCHANT, PATRICE PERNY 19.1. INTRODUCTION 779 19.2. INTRODUCTORY
EXAMPLES ; 780 19.2.1. UNINOMINAL SYSTEMS 781 19.2.2. SYSTEMS BASED ON
RANKINGS 786 19.3. SOME THEORETICAL RESULTS 789 19.3.1. ARROW S THEOREM
789 19.3.1.1. ARROW S THEOREM AND FUZZY PREFERENCES 794 19.3.2. SOME
OTHER RESULTS 795 19.3.2.1. IMPOSSIBILITY RESULTS 796 19.3.2.2.
CHARACTERIZATIONS 796 19.3.2.3. GENERALIZATIONS OF THE BORDA METHOD 799
19.3.2.4. A CHARACTERIZATION OF SIMPLE MAJORITY 799 19.3.2.5. ANALYSIS
801 19.4. MULTICRITERIA DECISION AIDING AND SOCIAL CHOICE THEORY 801
19.4.1. RELEVANCE AND LIMITS OF SOCIAL CHOICE RESULTS 801 19.4.2. SOME
RESULTS IN CLOSE RELATION WITH MULTICRITERIA ANALYSIS 803 19.4.2.1.
TACTIC 803 19.4.2.2. MULTI-ATTRIBUTE VALUE THEORY (MAVT) 804 XX
DECISION-MAKING PROCESS 19.4.2.3. WEIGHTED SUM 804 19.4.2.4. ELECTRE AND
PROMETHEE 804 - 19.5. BIBLIOGRAPHY 805 CHAPTER 20. METRIC AND LATTICIAL
MEDIANS 811 OLIVIER HUDRY, BRUNO LECLERC, BERNARD MONJARDET, JEAN-PIERRE
BARTHELEMY 20.1. INTRODUCTION 811 20.1.1. MEDIANS IN GENERAL 811 20.1.2.
MEDIANS OF BINARY RELATIONS 812 20.1.3. MEDIANS IN LATTICES 813 20.2.
MEDIAN RELATIONS 814 20.2.1. THE MODEL 814 20.2.2. THE MEDIAN PROCEDURE
815 20.2.3. THE ^-MEDIANS OF A PROFILE OF RELATIONS 816 20.2.4. THE
.M-MEDIANS OF A PROFILE OF RELATIONS 820 20.2.5. THE T-MEDIANS OF A
PROFILE OF TOURNAMENTS 820 20.3. THE MEDIAN LINEAR ORDERS (^-MEDIANS) OF
A PROFILE OF LINEAR ORDERS . . 822 20.3.1. BINARY LINEAR PROGRAMMING
FORMULATION 822 20.3.2. FORMULATION USING WEIGHTED DIRECTED GRAPHS 824
20.3.3. EQUIVALENT FORMULATIONS FOR THE SEARCH OF A MEDIAN ORDER OF A
PROFILE OF LINEAR ORDERS 825 20.3.4. COMPLEXITY OF THE SEARCH OF A
MEDIAN ORDER OF A PROFILE OF LINEAR ORDERS 829 20.3.5. EXACT AND
APPROXIMATE METHODS 830 20.3.6. PROPERTIES OF MEDIAN ORDERS 833 20.4.
MEDIANS IN LATTICES AND SEMILATTICES 836 20.4.1. ORDERED STRUCTURES 837
20.4.2. SYMMETRIC DIFFERENCE DISTANCE IN SEMILATTICES AND REMOTENESS . .
840 20.4.3. MEDIANS IN MEDIAN SEMILATTICES 841 20.4.4. OTHER
SEMILATTICES 844 20.4.5. APPLICATIONS 845 20.5. CONCLUSION 846 20.6.
ACKNOWLEDGEMENTS 849 20.7. BIBLIOGRAPHY 849 LIST OF AUTHORS 857 INDEX
861
|
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| dewey-sort | 3658.403 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV025556826 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:36:26Z |
| institution | BVB |
| isbn | 9781848211162 |
| language | English |
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| physical | XXXIII, 868 S. graph. Darst. |
| publishDate | 2009 |
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| publisher | ISTE [u.a.] |
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| spelling | Concepts et méthodes pour l'aide á la décision Decision-making process concepts and methods ed. by Denis Bouyssou ... London [u.a.] ISTE [u.a.] 2009 XXXIII, 868 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index. - Enth. 20 Beitr. Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Entscheidungsprozess (DE-588)4121202-2 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Entscheidungsprozess (DE-588)4121202-2 s Entscheidungsfindung (DE-588)4113446-1 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Bouyssou, Denis (DE-588)170247384 edt http://www.gbv.de/dms/zbw/590136984.pdf lizenzfrei GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020156620&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Decision-making process concepts and methods Entscheidungsfindung (DE-588)4113446-1 gnd Entscheidungsprozess (DE-588)4121202-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4113446-1 (DE-588)4121202-2 (DE-588)4114528-8 |
| title | Decision-making process concepts and methods |
| title_alt | Concepts et méthodes pour l'aide á la décision |
| title_auth | Decision-making process concepts and methods |
| title_exact_search | Decision-making process concepts and methods |
| title_full | Decision-making process concepts and methods ed. by Denis Bouyssou ... |
| title_fullStr | Decision-making process concepts and methods ed. by Denis Bouyssou ... |
| title_full_unstemmed | Decision-making process concepts and methods ed. by Denis Bouyssou ... |
| title_short | Decision-making process |
| title_sort | decision making process concepts and methods |
| title_sub | concepts and methods |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Entscheidungsprozess (DE-588)4121202-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Entscheidungsfindung Entscheidungsprozess Mathematisches Modell |
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