Expected Value Of Perfect Information
In decision theory, the expected value of perfect information (EVPI) is the price that one would be willing to pay in order to gain access to perfect information.
The problem is modeled with a payoff matrix Rij in which the row index i describes a choice that must be made by the payer, while the column index j describes a random variable that the payer does not yet have knowledge of, that has probability pj of being in state j. If the payer is to choose i without knowing the value of j, the best choice is the one that maximizes the expected monetary value:
where
is the expected payoff for action i i.e. the expectation value, and
is choosing the maximum of these expectations for all available actions. On the other hand, with perfect knowledge of j, the player may choose a value of i that optimizes the expectation for that specific j. Therefore, the expected value given perfect information is
where is the probability that the system is in state j, and is the pay-off if one follows action i while the system is in state j. Here indicates the best choice of action i for each state j.
The expected value of perfect information is the difference between these two quantities,
This difference describes, in expectation, how much larger a value the player can hope to obtain by knowing j and picking the best i for that j, as compared to picking a value of i before j is known. Note: EV|PI is necessarily greater than or equal to EMV. That is, EVPI is always non-negative.
EVPI provides a criterion by which to judge ordinary mortal forecasters. EVPI can be used to reject costly proposals: if one is offered knowledge for a price larger than EVPI, it would be better to refuse the offer. However, it is less helpful when deciding whether to accept a forecasting offer, because one needs to know the quality of the information one is acquiring.
Read more about Expected Value Of Perfect Information: Example
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