In simplistic form, Expected Utility Theory (EUT) is a mathematical decision making process. Conventionally defined, it is a process where “a decision maker (DM) chooses between risky or uncertain prospects by comparing their expected utility values, i.e., the weighted sums obtained by adding the utility values of outcomes multiplied by their respective probabilities” (Mongin,2007, p.1). Simply put, a decision maker correlates the relative of risk or probability versus reward or potential outcome across multiple scenarios. The result, called the expected utility (U), is always represented as relative numerical score which can be used by managers use the resulting in a rational decision making process. Most often, scores are compared under the theory of maximization, with the highest relative U being the most correct decision (Lengwiler, 2008).
A Theory with Simple Beginnings and Complicated Implementation
Expected Utility Theory has roots deeply seated in history. Taking notes from as early as the 17th century, the first known person to have written about the value of utility was Pascal, who defined and quantified U while making his argument about the rationality of the existence of God in what has become known as Pascal’s wager (Lengwiler, 2008). Over the following centuries many theorists including Allais, Camerer, Dupuit, Gossen, Bernoilli, and von Neumamm have adapted, revised, or refined the basic principles set out by Pascal (Lengwiler, 2008; Mongin, 1997). Controversy regarding the classical interpretation of EUT falls into two general categories: differences in opinion of the proper mathematical equation to relate probability or risk to reward or the validity of the model based on the possibility of inadequate or non...
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...ry, properly select amongst the various mathematical models, and ensure that all data and assumptions are consistent with the selected model. In the face of basic decisions, leaders can quickly and easily apply the conventional interpretation provided they both make logical assumptions and properly predict the related probabilities or levels or risk.
Works Cited
Blavatskyy, P. R. (2007). Stochastic expected utility theory. Journal of Risk and Uncertainty, 34(3), 259. doi:http://dx.doi.org/10.1007/s11166-007-9009-6
Lengwiler, Y. (2008). The origins of expected utility theory. Retrieved from http://wwz.unibas.ch/fileadmin/wwz/redaktion/finance/personen/yvan/papers/lengwiler-09.pdf
Mongin, P. (1997). Expected utility theory. Retrieved from https://studies2.hec.fr/jahia/webdav/site/hec/shared/sites/mongin/acces_anonyme/page%20internet/O12.MonginExpectedHbk97.pdf
Epstein, Richard A. The Theory of Gambling and Statistical Logic. New York: Academic, 1977. Print.
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