Examples of using Utility function in English and their translations into Serbian
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Provides terminal emulation and utility functions.
Utility function is unique up to a positive monotonic transform.
People may attempt to quantify the evaluation of a model using a utility function.
Most utility functions used in modeling or theory are well-behaved.
Analysis can take into account the decision maker's(e.g., the company's)preference or utility function, for example.
Ordinal utility functions are unique up to increasing monotone transformations.
Neoclassical economics has largely retreated from using cardinal utility functions as the basis of economic behavior.
Utility functions are also related to risk measures, with the most common example being the entropic risk measure.
An efficient portfolio most preferred by an investor because its risk/reward characteristics approximate the investor's utility function.
Note that for u to be a utility function on X, it must be defined for every package in X.
Bernoulli argued that the paradox could be resolved if decision-makers displayed risk aversion andargued for a logarithmic cardinal utility function.
In some cases, utility functions can be replaced by the probability of achieving uncertain aspiration levels.
In order to simplify calculations, various alternative assumptions have been made concerning details of human preferences, andthese imply various alternative utility functions such as.
The utility function is concave in the positive region, reflecting the phenomenon of diminishing marginal utility. .
Other questions of what arguments ought to enter into a utility function are difficult to answer, yet seem necessary to understanding utility. .
Today's utility functions, expressing utility as a function of the amounts of the various goods consumed, are treated as either cardinal or ordinal, depending on whether they are or are not interpreted as providing more information than simply the rank ordering of preferences over bundles of goods, such as information regarding preference strength.
In some of these new theories, as in cumulative prospect theory, the St. Petersburg paradox again appears in certain cases,even when the utility function is concave, but not if it is bounded(Rieger& Wang 2006).
In his solution,he defines a utility function and computes expected utility rather than expected financial value(see for a review).
The complete class theorems,which show that all admissible decision rules are equivalent to the Bayesian decision rule for some utility function and some prior distribution(or for the limit of a sequence of prior distributions).
For any unbounded utility function, one can find a lottery that allows for a variant of the St. Petersburg paradox, as was first pointed out by Menger(Menger 1934).
By making some reasonable assumptions about the way choices behave, von Neumann and Morgenstern showed that if an agent can choose between the lotteries,then this agent has a utility function which can be added and multiplied by real numbers, which means the utility of an arbitrary lottery can be calculated as a linear combination of the utility of its parts.
Specifically for any utility function, there exists a hypothetical reference lottery with the expected utility of an arbitrary lottery being its probability of performing no worse than the reference lottery.
By making some reasonable assumptions about the way choices behave, von Neumann and Morgenstern showed that if an agent can choose between the lotteries,then this agent has a utility function such that the desirability of an arbitrary lottery can be calculated as a linear combination of the utilities of its parts, with the weights being their probabilities of occurring.
Decision trees, influence diagrams, utility functions, other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, public health, are examples of operations research or management science methods.
Advocates for the use of probability theory point to: the work of Richard Threlkeld Cox for justification of the probability axioms, the Dutch book paradoxes of Bruno de Finetti as illustrative of the theoretical difficulties that can arise from departures from the probability axioms, and the complete class theorems,which show that all admissible decision rules are equivalent to the Bayesian decision rule for some utility function and some prior distribution(or for the limit of a sequence of prior distributions).
The expected utility hypothesis posits that a utility function exists the sign of whose expected net change from accepting the gamble is a good criterion for real people's behavior.
The decision maker's attitude to risk is represented by utility functions andtheir attitude to trade-offs between conflicting objectives can be expressed using multi-attribute value functions or multi-attribute utility functions(if there is risk involved).
In more formal language:A von Neumann-Morgenstern utility function is a function from choices to the real numbers: u: X→ R{\displaystyle u\colon X\to\mathbb{R}} which assigns a real number to every outcome in a way that captures the agent's preferences over simple lotteries.
Analysis can take into account the decision maker's(e.g., the company's)preference or utility function, for example: The basic interpretation in this situation is that the company prefers B's risk and payoffs under realistic risk preference coefficients(greater than $400K-in that range of risk aversion, the company would need to model a third strategy,"Neither A nor B").