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Decision Making Under Risk: A Comparison of Bayesian and Fuzzy Set Methods
Author(s) -
Buckley James J.
Publication year - 1983
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1983.tb00117.x
Subject(s) - outcome (game theory) , decision maker , computer science , set (abstract data type) , bayesian probability , optimal decision , fuzzy logic , mathematical optimization , artificial intelligence , influence diagram , decision analysis , fuzzy set , machine learning , mathematics , operations research , decision tree , mathematical economics , programming language
A classical decision problem is considered where a decision maker is to choose one of a number of actions each offering different consequences. The outcome from a choice of action is uncertain because it depends on the existing state of Nature. Also, the outcome, once an action and state of Nature are specified, may be a vector or a random vector. The decision maker employs both Bayesian methods and fuzzy set techniques to handle the uncertainties. The decision maker is also allowed to use multiple, possibly conflicting, goals in order to determine his best strategy. The Bayesian method produces a set of undominated strategies to choose from, whereas the fuzzy set technique usually produces a unique optimal strategy.