Decision that leads to the best outcome in decision theory
This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. Please help improve this article by introducing more precise citations.(September 2018) (Learn how and when to remove this message)
An optimal decision is a decision that leads to at least as good a known or expected outcome as all other available decision options. It is an important concept in decision theory. In order to compare the different decision outcomes, one commonly assigns a utility value to each of them.
If there is uncertainty as to what the outcome will be but knowledge about the distribution of the uncertainty, then under the von Neumann–Morgenstern axioms the optimal decision maximizes the expected utility (a probability–weighted average of utility over all possible outcomes of a decision). Sometimes, the equivalent problem of minimizing the expected value of loss is considered, where loss is (–1) times utility. Another equivalent problem is minimizing expected regret.
"Utility" is only an arbitrary term for quantifying the desirability of a particular decision outcome and not necessarily related to "usefulness." For example, it may well be the optimal decision for someone to buy a sports car rather than a station wagon, if the outcome in terms of another criterion (e.g., effect on personal image) is more desirable, even given the higher cost and lack of versatility of the sports car.
The problem of finding the optimal decision is a mathematical optimization problem. In practice, few people verify that their decisions are optimal, but instead use heuristics to make decisions that are "good enough"—that is, they engage in satisficing.
A more formal approach may be used when the decision is important enough to motivate the time it takes to analyze it, or when it is too complex to solve with more simple intuitive approaches, such as many available decision options and a complex decision–outcome relationship.
An optimaldecision is a decision that leads to at least as good a known or expected outcome as all other available decision options. It is an important...
Markov decision processes, in continuous-time Markov decision processes we want to find the optimal policy or control which could give us the optimal expected...
three branches of decision theory: Normative decision theory: Concerned with the identification of optimaldecisions, where optimality is often determined...
the optimal policy in the last time period is specified in advance as a function of the state variable's value at that time, and the resulting optimal value...
to generate such optimal trees have been devised, such as ID3/4/5, CLS, ASSISTANT, and CART. Among decision support tools, decision trees (and influence...
algorithm where locally optimaldecisions are made at each node. Such algorithms cannot guarantee to return the globally optimaldecision tree. To reduce the...
choose the optimal action under the actual observed data to obtain a uniformly optimal one, whereas choosing the actual frequentist optimaldecision rule as...
same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends...
explained by the optimal foraging theory. In each case, there are costs, benefits, and limitations that ultimately determine the optimaldecision rule that the...
metric for hard decision Viterbi decoders. The squared Euclidean distance is used as a metric for soft decision decoders. Optimaldecision decoding algorithm...
moment rather than an optimal solution. Therefore, humans do not undertake a full cost-benefit analysis to determine the optimaldecision, but rather, choose...
is optimal - no algorithm can do better than the optimaldecision tree. Thus, this algorithm has the peculiar property that it is provably optimal although...
Prescriptive decision-making research focuses on how to make "optimal" decisions (based on the axioms of rationality), while descriptive decision-making research...
scenario involving optimal stopping theory that is studied extensively in the fields of applied probability, statistics, and decision theory. It is also...
regardless, individuals will always choose the option they value most. Decisions reach an optimum only when they are unanimous, when votes are not coerced and everyone...
I spend my money in order to maximize my utility?" It is a type of optimaldecision problem. It consists of choosing how much of each available good or...
finding the optimal set S o {\displaystyle S^{o}} of outcomes on which it is reasonable to bet and it gives explicit formula for finding the optimal fractions...
theory of Markov decision processes states that if π ∗ {\displaystyle \pi ^{*}} is an optimal policy, we act optimally (take the optimal action) by choosing...
identify a single "best" (optimal) outcome. Instead, it only identifies a set of outcomes that might be considered optimal, by at least one person. Formally...
exact solution to a POMDP yields the optimal action for each possible belief over the world states. The optimal action maximizes the expected reward (or...
In microeconomics, the expenditure minimization problem is the dual of the utility maximization problem: "how much money do I need to reach a certain level...
Pareto optimality is subjective and that any policy can be considered Pareto optimal, which they describe as undermining all previous optimality claims...
experimental design is to a certain extent based on the theory for making optimaldecisions under uncertainty. The aim when designing an experiment is to maximize...