LEADER 05307nam 2200637Ia 450 001 9910830096503321 005 20200722233234.0 010 $a1-282-68400-0 010 $a9786612684005 010 $a0-470-61187-1 010 $a0-470-61030-1 035 $a(CKB)2550000000005840 035 $a(EBL)477628 035 $a(SSID)ssj0000354058 035 $a(PQKBManifestationID)11281489 035 $a(PQKBTitleCode)TC0000354058 035 $a(PQKBWorkID)10302399 035 $a(PQKB)11389758 035 $a(MiAaPQ)EBC477628 035 $a(OCoLC)520990369 035 $a(EXLCZ)992550000000005840 100 $a20090122d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDecision-making process $eConcepts and methods$b[electronic resource] /$fedited by Denis Bouyssou ... [et al.] 210 $aHoboken, NJ $cJohn Wiley & Sons$d2009 215 $a1 online resource (904 p.) 225 1 $aISTE ;$vv.135 300 $aDescription based upon print version of record. 311 $a1-84821-116-3 320 $aIncludes bibliographical references and index. 327 $aDecision-making Process; Contents; Preface; Chapter 1. From Decision Theory to Decision-aiding Methodology; 1.1. Introduction; 1.2. History; 1.2.1. Genesis and youth; 1.2.2. Maturity; 1.3. Different decision-aiding approaches; 1.4. The decision-aiding process; 1.4.1. The problem situation; 1.4.2. The problem formulation; 1.4.3. The evaluation model; 1.4.4. The final recommendation; 1.5. Conclusion; 1.6. Acknowledgements; 1.7. Bibliography; Chapter 2. Binary Relations and Preference Modeling; 2.1. Introduction; 2.2. Binary relations; 2.2.1. Definitions; 2.2.2. Properties of a binary relation 327 $a2.2.3. Graphical representation of a binary relation2.2.4. Matrix representation of a binary relation; 2.2.5. Example; 2.3. Binary relations and preference structures; 2.4. Classical preference structures; 2.4.1. Total order; 2.4.1.1. Definition; 2.4.1.2. Numerical representation; 2.4.2. Weak orders; 2.4.2.1. Definition; 2.4.2.2. Numerical representation; 2.4.3. Classical problems; 2.4.3.1. Choosing on the basis of binary relation; 2.4.3.2. Aggregating preferences; 2.4.3.3. Particular structure of the set of objects; 2.5. Semi-orders and interval orders; 2.5.1. Semi-order; 2.5.1.1. Definition 327 $a2.5.1.2. Weak order associated with a semi-order2.5.1.3. Matrix representation; 2.5.1.4. Numerical representation; 2.5.2. Interval order; 2.5.2.1. Definition; 2.5.2.2. Weak orders associated with an interval order; 2.5.2.3. Matrix representation; 2.5.2.4. Numerical representation; 2.5.3. Remarks; 2.6. Preference structures with incomparability; 2.6.1. Partial order; 2.6.2. Quasi-order; 2.6.3. Synthesis; 2.7. Conclusion; 2.7.1. Other preference structures; 2.7.2. Other problems; 2.8. Bibliography; Chapter 3. Formal Representations of Uncertainty; 3.1. Introduction 327 $a3.2. Information: a typology of defects3.2.1. Incompleteness and imprecision; 3.2.2. Uncertainty; 3.2.3. Gradual linguistic information; 3.2.4. Granularity; 3.3. Probability theory; 3.3.1. Frequentists and subjectivists; 3.3.2. Conditional probability; 3.3.3. The unique probability assumption in the subjective setting; 3.4. Incompleteness-tolerant numerical uncertainty theories; 3.4.1. Imprecise probabilities; 3.4.2. Random disjunctive sets and belief functions; 3.4.3. Quantitative possibility theory; 3.4.3.1. Possibility theory and belief functions 327 $a3.4.3.2. Possibility theory and imprecise probabilities3.4.3.3. Clouds and generalized p-boxes; 3.4.3.4. Possibility-probability transformations; 3.4.4. Possibility theory and non-Bayesian statistics; 3.5. Qualitative uncertainty representations; 3.6. Conditioning in non-additive representations; 3.6.1. Conditional events and qualitative conditioning; 3.6.2. Conditioning for belief functions and imprecise probabilities; 3.7. Fusion of imprecise and uncertain information; 3.7.1. Non-Bayesian probabilistic fusion; 3.7.2. Bayesian probabilistic fusion; 3.7.3. Fusion in possibility theory 327 $a3.7.4. Fusion of belief functions 330 $aThis book provides an overview of the main methods and results in the formal study of the human decision-making process, as defined in a relatively wide sense. A key aim of the approach contained here is to try to break down barriers between various disciplines encompassed by this field, including psychology, economics and computer science. All these approaches have contributed to progress in this very important and much-studied topic in the past, but none have proved sufficient so far to define a complete understanding of the highly complex processes and outcomes. This book provides the reade 410 0$aISTE 606 $aDecision support systems 606 $aDecision making$xMathematical models 615 0$aDecision support systems. 615 0$aDecision making$xMathematical models. 676 $a658.4/03 676 $a658.403 701 $aBouyssou$b D$g(Denis)$0731351 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830096503321 996 $aDecision-making process$93928833 997 $aUNINA