Consensus Under Fuzziness

We live, unfortunately, in turbulent and difficult times plagued by various political, economic, and social problems, as well as by natural disasters worldwide. Systems become more and more complicated, and this concerns all levels, exemplified first by global political alliances, groups of countries, regions, etc., and secondly, by multinational (global) corporations and companies of all sizes. These same concerns affect all social groups. This all makes decision processes very complicated. In virtually all decision processes in these complicated systems, there are various actors (decision makers) who represent individual subjects (persons, countries, companies, etc.) and their respective interest groups. To reach a meaningful (good) decision, opinions of all such actors must be taken into account or a given decision may be rejected and not implemented. Ideally, a decision would be made after a consensus between the parties involved had been attained. So, consensus is a very desirable situation. In most real-world cases there is considerable uncertainty concerning all aspects of the decision making process. Moreover, opinions, goals, constraints, etc. are usually imprecisely known. This makes the decision making process difficult as one cannot employ conventional "hard" tools.

Contenu

Preface. 1: Introductions. Consensus, Negotiation and Mediation; K. Lehrer. Fuzziness and the Normative Theory of Social Choice; P.K. Pattanaik. Types and Measures of Uncertainty; G.J. Klir, D. Harmanec. 2: Tools and Techniques for Measuring and Monitoring Consensus Reaching. `Soft' Degrees of Consensus under Fuzzy Preferences and Fuzzy Majorities; J. Kacprzyk, et al. An Approach to the Consensus Reaching Support in Fuzzy Environment; S. Zadrozny. The Dichotomous Approach to Soft Consensus Measurement; S. Greco. Consensus Based on Fuzzy Coincidence for Group Decision Making in Linguistic Setting; F. Herrera, et al. Modeling Preference Relations and Consensus in a Linguistic Environment: An Approach Based on OWA Operators; G. Bordogna, et al. 3: New Paradigms and Architectures for Modeling Consensus Reaching. Protocol for Negotiations Among Multiple Intelligent Agents; R.R. Yager. The Development of Fuzzy Consensus Via Neural Modelling; W. Pedrycz. 4: Auxiliary Formal Tools and Techniques for Modeling Consensus Reaching. Consensus for Decomposable Measures; J. Fodor, et al. Construction of Fuzzy Utility Functions in Group Decision Making; F. Seo. Problem Solving with Multiple Interdependent Criteria; Ch. Carlsson, R. Fuller. Lexicographical Solutions in n-Person Cooperative Games with Multiple Scenarios; M. Sakawa, I. Nishizaki. 5: Applications and Case Studies. Identification of Ideological Dimensions under Fuzziness: The Case of Poland; J. Holubiec, et al. Determining Weights of Research Topics on the Basis of Expert Judgements. The Case of Systems Research Institute; D. Wagner. Index.