Fuzzy Set Theory and U.S. Arms Transfers: Modeling the Decision-Making Process

1988 ◽  
Vol 32 (4) ◽  
pp. 1018 ◽  
Author(s):  
Gregory S. Sanjian
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Decision Making Process ◽  
Arms Transfers

Fuzzy Decision Making in Politics: A Linguistic Fuzzy-Set Approach (LFSA)

2005 ◽  
Vol 13 (1) ◽  
pp. 23-56 ◽  
Author(s):  
Badredine Arfi
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Trust And Power

In this article I use linguistic fuzzy-set theory to analyze the process of decision making in politics. I first introduce a number of relevant elements of (numerical and linguistic) fuzzy-set theory that are needed to understand the terminology as well as to grasp the scope and depth of the approach. I then explicate a linguistic fuzzy-set approach (LFSA) to the process of decision making under conditions in which the decision makers are required to simultaneously satisfy multiple criteria. The LFSA approach is illustrated through a running (hypothetical) example of a situation in which state leaders need to decide how to combine trust and power to make a choice on security alignment.

Evaluation of Quantified Statements Using Gradual Numbers

2011 ◽  
pp. 246-269 ◽  
Author(s):  
Ludovic Liétard ◽  
Daniel Rocacher
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Relational Databases ◽  
Fuzzy Numbers ◽  
Theoretical Background ◽  
Definition Of ◽  
Flexible Querying

This chapter is devoted to the evaluation of quantified statements which can be found in many applications as decision making, expert systems, or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type “Q X are A” and “Q B X are A.” In this context, quantified statements are interpreted using an arithmetic on gradual numbers from Nf, Zf, and Qf. It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches.

Decision Making Approach using Weighted Coefficient of Correlation along with Generalized Parametric Fuzzy Entropy Measure

2016 ◽  
Vol 5 (3) ◽  
pp. 30-41 ◽  
Author(s):  
Priti Gupta ◽  
Pratiksha Tiwari
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Entropy Measure ◽  
Managerial Decision ◽  
Coefficient Of Correlation ◽  
Different Types ◽  
Segmentation Of Images ◽  
Real World Problems

Decision making involves various attributes along with several decision takers. Recently it has become more complex. This gives raise to uncertainty and associated with the information provided. So it may be appropriate to suggest that uncertainty demonstrates itself in numerous forms and of different types. Uncertainties may arise due to human behaviour, fluctuations of information, unknown facts. Fuzzy set theory is tool to deal with uncertainty in a better way. Both Fuzzy set theory and information theory are involved in dealing with various real-world problems such as segmentation of images, medical diagnosis, managerial decision making etc. Several methods and concepts dealing with imprecision and uncertainty have been proposed by many researchers. In the present communication, the authors have proposed a parametric generalization of entropy introduced by De Luca and Termini along with its basic properties. Further, a new measure of weighted coefficient of correlation is developed and applied to solve decision making problems involving uncertainty.

Test of a Fuzzy Set Based Decision Model as an AID in Solving Human Factors Design Problems

1981 ◽  
Vol 25 (1) ◽  
pp. 306-310
Author(s):  
Richard A. Newman
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Decision Aid ◽  
Decision Model ◽  
Decision Rules ◽  
Design Problems ◽  
Weighting Rule

Fuzzy Set Theory has proved popular for development of decision making models. However, most such models have not been tested using problems such as commonly found in Human Factors system design. This study used a decision model that combined Fuzzy Set decision rules with an eigenvector weighting rule. Five experienced Human Factors Designers solved six design problems, half manually, and half using a computer program that served as a decision making aid, using the model. On completion of the procedure, the computer model made a recommendation for a solution. The user could accept or reject the model's choice. Comparisons were made between manual and computer aided decision making, and the Fuzzy Set decision rule was compared with other possible decision rules using the same data. Results showed that use of the model-based decision aid was accepted by the users, and were reasonable. In addition, a possible measure of decision making quality was found in the measure of weighting inconsistency which is part of the eigenvector procedure.

scholarly journals Fuzzy Insurance

1990 ◽  
Vol 20 (1) ◽  
pp. 33-55 ◽  
Author(s):  
Jean Lemaire
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Life Insurance ◽  
Fuzzy Set Theory ◽  
Fuzzy Numbers ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Basic Concepts ◽  
Definition Of

AbstractFuzzy set theory is a recently developed field of mathematics, that introduces sets of objects whose boundaries are not sharply defined. Whereas in ordinary Boolean algebra an element is either contained or not contained in a given set, in fuzzy set theory the transition between membership and non-membership is gradual. The theory aims at modelizing situations described in vague or imprecise terms, or situations that are too complex or ill-defined to be analysed by conventional methods. This paper aims at presenting the basic concepts of the theory in an insurance framework. First the basic definitions of fuzzy logic are presented, and applied to provide a flexible definition of a “preferred policyholder” in life insurance. Next, fuzzy decision-making procedures are illustrated by a reinsurance application, and the theory of fuzzy numbers is extended to define fuzzy insurance premiums.

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