Paper Info
Title
Fuzzy Preferences in the Graph Model for Conflict Resolution
Abstract
new framework for the graph model for conflict resolution is developed so that decision makers (DMs) with fuzzy preferences can be included in conflict models. A graph model is both a formal representation for multiple participant–multiple objective decision problems and a set of analysis procedures that add insights into them. Within the new framework, graph models can include—and integrate into the analysis—both certain and uncertain information about DMs’ preferences. One key contribution of this study is to extend the four basic stability definitions for two or more DMs to models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide a nuanced description of human behavior. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome which the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is the characteristic of the DM. A fuzzy equilibrium, which is an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the strategic conflict. The practical application and interpretation of these new stability definitions are illustrated with an example.
Year
DOI
Venue
2012
10.1109/TFUZZ.2012.2183603
IEEE Transactions on Fuzzy Systems
Keywords
Field
DocType
decision making,fuzzy set theory,game theory,graph theory,DM,analysis procedures,conflict resolution,decision makers,fuzzy Nash stability,fuzzy equilibrium,fuzzy general metarationality,fuzzy preferences,fuzzy satisficing threshold,fuzzy sequential stability,fuzzy symmetric metarationality,graph model,multiple participant-multiple objective decision problems,stability definitions,Conflict,environmental conflict,fuzzy equilibrium (FE),fuzzy preference,fuzzy relative strength of preference (FRSP),fuzzy satisficing threshold (FST),fuzzy stability,fuzzy unilateral improvement (FUI),graph model
Mathematical optimization,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy measure theory,Fuzzy logic,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy associative matrix,Fuzzy number,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
20
4
1063-6706
Citations 
PageRank 
References 
8
0.60
19
Authors
3
Name
Order
Citations
PageRank
M. Abul Bashar1252.42
D. Marc Kilgour257170.61
K. W. Hipel3812116.70