Value of Information

Value of information measures expected improvement from choosing after additional information within a specified decision model. Net value also accounts for acquisition costs, delay, and practical constraints.

Decision-theoretic value of information compares the expected value of choosing after a signal with the best choice using current information. It accounts for how possible signals change beliefs and available actions. Perfect information provides an upper bound within the specified decision model, while realistic information may be noisy, delayed, or only partially relevant.

If every possible signal leaves the same action optimal, information has no instrumental value for that fixed decision and payoff model. It may still matter for future decisions, intrinsic understanding, or other values omitted from the model. Acquisition costs, delay, and implementation burdens must be compared with the expected gain rather than assuming uncertainty reduction is automatically worthwhile.

When to use it

When deciding whether to gather more information; when research effort needs proportioning; when analysis paralysis needs a framework for 'enough'; when understanding that information only has value when it could change the decision.

How it can help

Define the choices and uncertainties, model how possible signals would change the action, and compare the expected gain with the cost. Check whether future decisions or other values are omitted.