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|a (JST)2634375
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|a eng
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|a Myung, In Jae
|e verfasserin
|4 aut
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|a Maximum Entropy Aggregation of Expert Predictions
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|c 1996
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|a Text
|b txt
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|a Computermedien
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|a This paper presents a maximum entropy framework for the aggregation of expert opinions where the expert opinions concern the prediction of the outcome of an uncertain event. The event to be predicted and individual predictions rendered are assumed to be discrete random variables. A measure of expert competence is defined using a distance metric between the actual outcome of the event and each expert's predicted outcome. Following Levy and Deliç (1994), we use Shannon's information measure (Shannon 1948, Jaynes 1957) to derive aggregation rules for combining two or more expert predictions into a single aggregated prediction that appropriately calibrates different degrees of expert competence and reflects any dependence that may exist among the expert predictions. The resulting maximum entropy aggregated prediction is least prejudiced in the sense that it utilizes all information available but remains maximally non-committal with regard to information not available. Numerical examples to illuminate the implications of maximum entropy aggregation are also presented.
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|a Copyright 1996 Institute for Operations Research and the Management Sciences
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|a Consensus
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|a Expert Opinion
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|a Maximum Entropy
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|a Aggregation
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|a Information Theory
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|a Decision Aids
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|a Physical sciences
|x Physics
|x Thermodynamics
|x Thermodynamic properties
|x Entropy
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|a Philosophy
|x Metaphysics
|x Ontology
|x Atomism
|x Aggregation
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Statistical distributions
|x Distribution functions
|x Probability distributions
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|a Behavioral sciences
|x Psychology
|x Cognitive psychology
|x Cognitive processes
|x Thought processes
|x Reasoning
|x Inference
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Statistical distributions
|x Distribution functions
|x Probability distributions
|x Mathematical moments
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|a Mathematics
|x Pure mathematics
|x Probability theory
|x Probabilities
|x Conditional probabilities
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|a Mathematics
|x Mathematical expressions
|x Axioms
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Expected values
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|a Economics
|x Economic disciplines
|x Financial economics
|x Finance
|x Interest
|x Interest rates
|x Base interest rates
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Random events
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|a research-article
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|a Ramamoorti, Sridhar
|e verfasserin
|4 aut
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|a Bailey,, Andrew D.
|e verfasserin
|4 aut
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|i Enthalten in
|t Management Science
|d Institute for Operations Research and the Management Sciences, 1954
|g 42(1996), 10, Seite 1420-1436
|w (DE-627)320623602
|w (DE-600)2023019-9
|x 15265501
|7 nnns
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|g volume:42
|g year:1996
|g number:10
|g pages:1420-1436
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|u https://www.jstor.org/stable/2634375
|3 Volltext
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|d 42
|j 1996
|e 10
|h 1420-1436
|