{"title":"Binary Relations and Preference Modeling","authors":"D. Bouyssou, P. Vincke","doi":"10.1002/9780470611876.CH2","DOIUrl":"https://doi.org/10.1002/9780470611876.CH2","url":null,"abstract":"The literature on preference modeling is vast. This can first be explained by the fact that the question of modeling preferences occurs in sev eral disciplines, e.g. – in Economics, where one tries to model the preferences of a ‘ rational consumer’ [e.g. DEB 59]; – in Psychology in which the study of preference judgments co lle ted in experiments is quite common [KAH 79, KAH 81]; – in Political Sciences in which the question of defining a col lective preference on the basis of the opinion of several voters is central [SEN 86] ; – in Operational Research in which optimizing an objective f unction implies the definition of a direction of preference [ROY 85]; and – in Artificial Intelligence in which the creation of autonomus agents able to take decisions implies the modeling of their vision of what is des irable and what is less so [DOY 92].","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Formal Representations of Uncertainty","authors":"D. Dubois, H. Prade","doi":"10.1002/9780470611876.CH3","DOIUrl":"https://doi.org/10.1002/9780470611876.CH3","url":null,"abstract":"The recent development of uncertainty theories that account for the notion of belief is linked to the emergence, in the XXth century, of Decision Theory and Artificial Intelligence. Nevertheless, this topic was dealt with very differently by each area. Decision Theory insisted on the necessity to found representations on the empirical observation of individuals choosing between courses of action, regardless of any other type of information. Any axiom in the theory should be liable of empirical validation. Probabilistic representations of uncertainty can then be justified with a subjectivist point of view, without necessary reference to frequency. Degrees of probability then evaluate to what extent an agent believes in the occurrence of an event or in the truth of a proposition. In contrast, Artificial Intelligence adopted a more introspective approach aiming at formalizing intuitions, reasoning processes, through the statement of reasonable axioms, often without reference to probability. Actually, until the nineties Artificial Intelligence essentially focused on purely qualitative and ordinal (in fact, logical) representations.","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124677555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conjoint Measurement Models for Preference Relations","authors":"D. Bouyssou, M. Pirlot","doi":"10.1002/9780470611876.CH16","DOIUrl":"https://doi.org/10.1002/9780470611876.CH16","url":null,"abstract":"Conjoint measurement [KRA 71, WAK 89] is concerned with the study of binary relations defined on Cartesian products of sets. Such relations are central in many disciplines, for example:– multicriteria or multiattribute decision making, in which the preference of the decision maker is a relation that encodes, for each pair of alternatives, the preferred option taking into account all criteria [BEL 01, KEE 76, WIN 86];– decision under uncertainty, where the preference relation compares alternatives evaluated on several states of nature [FIS 88, GUL 92, SHA 79, WAK 84, WAK89];– consumer theory, dealing with preference relations that compare bundles of goods [DEB 59];– inter-temporal decision making, that uses preference relations for comparing alternatives evaluated at various instants in time [KOO 60, KOO 72, KEE 76]; and– inequality measurement, that compares distributions of wealth across individuals [ATK 70, BEN 94, BEN 97]","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129381105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Survey of Qualitative Decision Rules under Uncertainty","authors":"D. Dubois, H. Fargier, H. Prade, R. Sabbadin","doi":"10.1002/9780470611876.CH11","DOIUrl":"https://doi.org/10.1002/9780470611876.CH11","url":null,"abstract":"","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"112 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multiattribute Utility Theory","authors":"M. Abdellaoui, Christophe Gonzales","doi":"10.1002/9780470611876.CH15","DOIUrl":"https://doi.org/10.1002/9780470611876.CH15","url":null,"abstract":"Important decisions for both individuals and organizations often take into account multiple objectives. From the purchase of a family car to the choice of the most appropriate localization of a nuclear plant, decision makers’ choices depend on many different objectives. Most often, the multiobjective nature of important decisions is revealed by assertions like “we are willing to pay a little bit more to gain the comfort or prestige of brand A instead of that of brand B” in the case of a car purchase; or “we agree to increase a little the access time to the airport if, in return, the possibilities of its future extension are increased as well, or if this can reduce noise pollution for residents” in the case of the localization of a new airport. These statements involve tradeoffs between different objectives of the decision maker. These tradeoffs result either from an introspective consideration performed by the decision maker herself or from an explicit decision aiding process in which the decision maker expresses her will to make coherent tradeoffs in order to make the “best” possible decision. The first attempts of multiple objective decision aiding date back to the 60’s with the works by, e.g., Raiffa and Edwards [Rai69, Edw71], which gave birth to Decision Analysis. In these works, the decision maker’s preferences are represented numerically on the set of all possible choices using a numerical function called a utility function (or “utility” for short). The key idea of this approach lies in the fact that, after a utility function has been elicited (i.e., constructed) in a simple decision context, it can be used to assign “scores” or utilities to all potential actions (i.e., the possible choices) that the decision maker faces. These scores can thus be used to rank the actions from the least desirable to the most desirable one (and conversely). However, the very fact that such scores can be constructed requires two different kinds of conditions to hold. The first one concerns “coherence conditions” that must be satisfied by the decision maker’s preferences for the latter to be numerically representable by a utility function. The second condition concerns other constraints that must be satisfied in order for the initial multiobjective utility function to be decomposable as a “simple combination” of mono-objective utility functions (these are also called multiattribute and single-attribute utility functions respectively). The limited cognitive abilities of decision makers make","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126546166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Cognitive Approach to Human Decision Making","authors":"E. Raufaste, D. Hilton","doi":"10.1002/9780470611876.CH12","DOIUrl":"https://doi.org/10.1002/9780470611876.CH12","url":null,"abstract":"","PeriodicalId":112888,"journal":{"name":"Decision-making Process","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131668833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}