decisionrelevance

Decision relevance is a concept in decision analysis and information theory that describes the extent to which information, observations, or events influence the choice of action and its expected outcomes within a given decision model. It rests on how the information would alter preferences, utilities, or beliefs that drive the decision.

A piece of information is considered decision-relevant if it can change the recommended action or the resulting expected utility under the model’s assumptions. If an observation leaves the optimal choice unchanged, it is deemed decision-irrelevant within that context. Relevance depends on the decision problem, including the set of alternatives, the loss or utility function, and the uncertainty represented by beliefs or data.

Decision relevance is closely linked to the value of information. Measures such as the expected value of perfect information (EVPI) or the expected value of sample information (EVSI) quantify how much obtaining additional information could improve the decision. Qualitative judgment and sensitivity analysis are also common tools for assessing relevance, especially when models are complex or data are noisy.

Applications of decision relevance span business, policy, engineering, and statistics. For example, market forecasts that shift the most valuable product launch region demonstrate decision-relevant information, whereas benign signals that do not affect the optimal action do not.

Limitations include dependence on the chosen model, assumptions about utilities, and static versus dynamic decision contexts. What is decision-relevant in one setting may be irrelevant in another, underscoring the context-dependent nature of the concept.