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The calculation of the value of perfect information does not rely on having drawn a decision tree.
Normally, when making decisions using expected values, you calculate the expected value of each choice and choose whichever has the highest expected value.
However, with perfect information you would already know which of the uncertain outcomes would occur and so you can decide in advance which would be the best course of action (and therefore the return) for each of the possible outcomes.
Then you can work out the expected value of these. The difference between this expected value and the expected value without the information (i.e. the ‘normal’ expected value calculation) is the value of the perfect information.It is difficult to explain in a few lines here, but have you watched the lecture on this site?
Sir, I was unable to find the lecture video about the value of perfect information under F5 lecture page, I still have serious doubts on this topic wanted to clear the doubts….could you help in telling me where it is posted..thanks a lot in advance….Sreekumar F5 student for the Dec1 exam.
Sorry, I was mistaken 🙁
There is no lecture yet on perfect information. However there is an example in our free Course Notes (and of course the answer is at the back of the notes).
Thanks a lot for a quick reply..No worriesi will manage with the course notes…sreekumar
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