Decision rule

This article is about decision theory. For the use in computer science, see decision rules.

In decision theory, a decision rule is a function which maps an observation to an appropriate action. Decision rules play an important role in the theory of statistics and economics, and are closely related to the concept of a strategy in game theory.

In order to evaluate the usefulness of a decision rule, it is necessary to have a loss function detailing the outcome of each action under different states.

Formal definition

Given an observable random variable X over the probability space $\scriptstyle (\mathcal{X},\Sigma, P_\theta)$, determined by a parameter θ ∈ Θ, and a set A of possible actions, a (deterministic) decision rule is a function δ : $\scriptstyle\mathcal{X}$→ A.

Examples of decision rules

• An estimator is a decision rule used for estimating a parameter. In this case the set of actions is the parameter space, and a loss function details the cost of the discrepancy between the true value of the parameter and the estimated value.
• Out of sample prediction in regression and classification models.

See also

• Admissible decision rule
• Bayes estimator