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555
Prospect theory: An analysis of decisions under risk
 Econometrica
, 1979
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Cited by 6283 (30 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Learning Bayesian networks: The combination of knowledge and statistical data
 Machine Learning
, 1995
"... We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simpl ..."
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Cited by 1155 (35 self)
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We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user’s prior knowledge. In particular, a user can express his knowledge—for the most part—as a single prior Bayesian network for the domain. 1
The Transferable Belief Model
 ARTIFICIAL INTELLIGENCE
, 1994
"... We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions ..."
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Cited by 488 (16 self)
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We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions and are quantified by probability functions. The relation between the belief function and the probability function when decisions must be made is derived and justified. Four paradigms are analyzed in order to compare Bayesian, upper and lower probability, and the transferable belief approaches.
A Tutorial on Learning Bayesian Networks
 Communications of the ACM
, 1995
"... We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by c ..."
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Cited by 364 (12 self)
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We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by combining domain knowledge with statistical data. 1 Introduction Many techniques for learning rely heavily on data. In contrast, the knowledge encoded in expert systems usually comes solely from an expert. In this paper, we examine a knowledge representation, called a Bayesian network, that lets us have the best of both worlds. Namely, the representation allows us to learn new knowledge by combining expert domain knowledge and statistical data. A Bayesian network is a graphical representation of uncertain knowledge that most people find easy to construct and interpret. In addition, the representation has formal probabilistic semantics, making it suitable for statistical manipulation (Howard,...
Fourier descriptors for plane closed curves,”
 IEEE Transactions on Computers,
, 1972
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Curvature of the probability weighting function
 Management Science
, 1996
"... Empirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function. We ..."
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Cited by 288 (5 self)
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Empirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function. We present a nonparametric estimation procedure for assessing the probability weighting function and value function at the level of the individual subject. The evidence in the domain of gains supports a twoparameter weighting function, where each parameter is given a psychological interpretation: one parameter measures how the decision maker discriminates probabilities, and the other parameter measures how attractive the decision maker views gambling. These findings are consistent with a growing body of empirical and theoretical work attempting to establish a psychological rationale for the probability weighting function. ª 1999 Academic Press The perception of probability has a psychophysics all its own. If men have a 2 % chance of contracting a particular disease and women have a 1% chance, we perceive the risk for men as twice the risk for women. However, the same difference of 1 % appears less dramatic when the chance of con
Some issues on consistency of fuzzy preference relations.
 European Journal of Operational Research
, 2004
"... Abstract In decision making, in order to avoid misleading solutions, the study of consistency when the decision makers express their opinions by means of preference relations becomes a very important aspect. In decision making problems based on fuzzy preference relations the study of consistency is ..."
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Abstract In decision making, in order to avoid misleading solutions, the study of consistency when the decision makers express their opinions by means of preference relations becomes a very important aspect. In decision making problems based on fuzzy preference relations the study of consistency is associated with the study of the transitivity property. In this paper, a new characterization of the consistency property defined by the additive transitivity property of the fuzzy preference relations is presented. Using this new characterization a method for constructing consistent fuzzy preference relations from a set of n1 preference data is proposed. Applying this method it is possible to assure better consistency of the fuzzy preference relations provided by the decision makers, and in such a way, to avoid the inconsistent solutions in the decision making processes. Additionally, a similar study of consistency is developed for the case of multiplicative preference relations.
Multidimensional poverty indices
 Social Choice and Welfare
, 2002
"... Abstract. This paper explores the axiomatic foundation of multidimensional poverty indices. Departing from the income approach which measures poverty by aggregating shortfalls of incomes from a predetermined povertyline income, a multidimensional index is a numerical representation of shortfalls o ..."
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Cited by 77 (0 self)
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Abstract. This paper explores the axiomatic foundation of multidimensional poverty indices. Departing from the income approach which measures poverty by aggregating shortfalls of incomes from a predetermined povertyline income, a multidimensional index is a numerical representation of shortfalls of basic needs from some prespeci®ed minimum levels. The class of subgroup consistent poverty indices introduced by Foster and Shorrocks �1991) is generalized to the multidimensional context. New concepts necessary for the design of distributionsensitive multidimensional poverty measures are introduced. Speci®c classes of subgroup consistent multidimensional poverty measures are derived based on sets of reasonable axioms. This paper also highlights the fact that domain restrictions may have a critical role in the design of multidimensional indices. 1