Introduction
In every field of human endeavor, individuals and organizations make decisions under conditions of uncertainty and ignorance. The consequences of a decision and their value to the decision maker often depend on events or quantities which are unknown to the decision maker at the time the choice must be made. Such problems of decision under uncertainty form the subject matter of Bayesian decision theory. Bayesian decision theory has been applied to problems in a broad variety of fields, including engineering, economics, business, public policy, and artificial intelligence.
A decision-theoretic model for a problem of decision under uncertainty contains the following basic elements:
A set of options from which the decision maker may choose;
A set of consequences that may occur as a result of the decision;
A probability distribution that quantifies the decision maker's beliefs about the consequences that may occur if each of the options is chosen; and
A utility function that...
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Laskey, K.B. (2013). Bayesian Decision Theory, Subjective Probability, and Utility. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_66
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DOI: https://doi.org/10.1007/978-1-4419-1153-7_66
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