[1] A common discipline that uses the EVPI concept is health economics.
The expected value of perfect information analysis tries to measure the expected cost of that uncertainty, which “can be interpreted as the expected value of perfect information (EVPI), since perfect information can eliminate the possibility of making the wrong decision” at least from a theoretical perspective.
[2] The problem is modeled with a payoff matrix Rij in which the row index i describes a choice that must be made by the player, while the column index j describes a random variable that the player does not yet have knowledge of, that has probability pj of being in state j.
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.
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.
EVPI provides a criterion by which to judge ordinary imperfectly informed forecasters.
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.
Solution: Here the payoff matrix is: The probability vector is: Expectation for each vehicle (
Thus, On the other hand, consider if we did know ahead of time which way the market would turn.
Given the knowledge of the direction of the market we would (potentially) make a different investment vehicle decision.
Hence, Conclusion: Knowing the direction the market will go (i.e. having perfect information) is worth $350.