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Although there are at least four sources of uncertainty - the attribute outcomes, and a decisionmaker's fuzziness about: a) the specific shapes of the individual attribute utility functions, b) the aggregating constants' values, and c) whether the attribute utility functions are additive, these terms being addressed presently - uncertainty ...
This variably curving utility function would thereby explain why an individual is risk-loving when he has more wealth (e.g., by playing the lottery) and risk-averse when he is poorer (e.g., by buying insurance). The function has been used widely, including in the field of economic history to explain why social gambling did not necessarily mean ...
A single-attribute utility function maps the amount of money a person has (or gains), to a number representing the subjective satisfaction he derives from it. The motivation to define a utility function comes from the St. Petersburg paradox: the observation that people are not willing to pay much for a lottery, even if its expected monetary gain is infinite.
E.g., the commodity is a heterogeneous resource, such as land. Then, the utility functions are not functions of a finite number of variables, but rather set functions defined on Borel subsets of the land. The natural generalization of a linear utility function to that model is an additive set function.
In decision theory, the von Neumann–Morgenstern (VNM) utility theorem demonstrates that rational choice under uncertainty involves making decisions that take the form of maximizing the expected value of some cardinal utility function. This function is known as the von Neumann–Morgenstern utility function.
When faced with several alternatives, the person will choose the alternative with the highest utility. The utility function is not visible; however, by observing the choices made by the person, we can "reverse-engineer" his utility function. This is the goal of revealed preference theory. [citation needed] In practice, however, people are not ...
While there are several ways to derive the Slutsky equation, the following method is likely the simplest. Begin by noting the identity (,) = (, (,)) where (,) is the expenditure function, and u is the utility obtained by maximizing utility given p and w.
Exponential utility implies constant absolute risk aversion (CARA), with coefficient of absolute risk aversion equal to a constant: ″ ′ =. In the standard model of one risky asset and one risk-free asset, [1] [2] for example, this feature implies that the optimal holding of the risky asset is independent of the level of initial wealth; thus on the margin any additional wealth would be ...