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In economics, a random utility model (RUM), [1] [2] also called stochastic utility model, [3] is a mathematical description of the preferences of a person, ...
Stochastic dominance is a partial order between random variables. [1] [2] It is a form of stochastic ordering.The concept arises in decision theory and decision analysis in situations where one gamble (a probability distribution over possible outcomes, also known as prospects) can be ranked as superior to another gamble for a broad class of decision-makers.
In economics, King–Plosser–Rebelo preferences are a particular functional form of utility that is used in many macroeconomic models and dynamic stochastic general equilibrium models. Having originally been proposed in an article that appeared in the Journal of Monetary Economics in 1988, [ 1 ] the corresponding technical appendix detailing ...
A utility function other than CRRA can be used. Transaction costs can be introduced. For proportional transaction costs the problem was solved by Davis and Norman in 1990. [6] It is one of the few cases of stochastic singular control where the solution is known.
In the case where the maximization is an integral of a concave function of utility over an horizon (0,T), dynamic programming is used. There is no certainty equivalence as in the older literature, because the coefficients of the control variables—that is, the returns received by the chosen shares of assets—are stochastic.
The CAPM can be derived from the following special cases of the CCAPM: (1) a two-period model with quadratic utility, (2) two-periods, exponential utility, and normally-distributed returns, (3) infinite-periods, quadratic utility, and stochastic independence across time, (4) infinite periods and log utility, and (5) a first-order approximation ...
Stochastic forensics analyzes computer crime by viewing computers as stochastic steps. In artificial intelligence , stochastic programs work by using probabilistic methods to solve problems, as in simulated annealing , stochastic neural networks , stochastic optimization , genetic algorithms , and genetic programming .
Suppose the objective is to maximize the expected utility of this wealth at the last period, that is, to consider the problem [()]. This is a multistage stochastic programming problem, where stages are numbered from = to =. Optimization is performed over all implementable and feasible policies.