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 which may occur as a result of the decision;
A probability distribution which quantifies the decision maker's beliefs about the consequences that may occur if each of the options is chosen; and
A utility functionwhich quantifies...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bayes, T.R. (1763). “An Essay Towards Solving a Problem in the Doctrine of Chances,” Philosophical Transactions of the Royal Society of London 53, 370–418 (reprinted with biographical note by G. Barnard, 1958, in Biometrika 45, 293–315).
Bernoulli, D. (1738). “Specimen Theoriae Novae de Mensura Sortis,” Commentarii Academiae Scientarium Imperalis Petropolitanae 175–192 (translated in L. Sommer, 1984, Econometrica, 22, 23–26).
Clemen, R. (1996). Making Hard Decisions, Duxbury Press, Belmont, California.
de Finetti, B. (1974). Theory of Probability: A Critical Introductory Treatment. John Wiley, New York.
De Groot, M.H. (1970). Optimal Statistical Decisions. McGraw Hill, New York.
Fine, T.L. (1973). Theories of Probability. Academic Press, New York.
Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis. Chapman and Hall, London.
Haddawy, P. (1994). “Generating Bayesian Networks from Probabilistic Knowledge Bases,” Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Mateo, California, 262–299.
Haddawy, P. (1999). “An Overview of Some Recent Developments in Bayesian Problem Solving,” AI Magazine 20(2), 11–19.
Heckerman, D. (1991). “Probabilistic Similarity Networks,” Ph.D. diss., Program in Medical Information Sciences, Stanford University, California.
Pratt, J.W., Raiffa, H., and Schlaifer, R. (1965). The Foundations of Decision Under Uncertainty: An Elementary Exposition. McGraw Hill, New York.
von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press, New Jersey.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Kluwer Academic Publishers
About this entry
Cite this entry
Laskey, K.B. (2001). BAYESIAN DECISION THEORY, SUBJECTIVE PROBABILITY AND UTILITY. In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_66
Download citation
DOI: https://doi.org/10.1007/1-4020-0611-X_66
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-7923-7827-3
Online ISBN: 978-1-4020-0611-1
eBook Packages: Springer Book Archive