Richard at TaoSecurity is addressing FAIR again. This time I have come up with what I think is a pretty good argument in defense of FAIR. I wrote a comment at Richard's post but I cite it below as well:

Richard,What do you think?

I think you are right in some aspects, that is: since with FAIR you do not usually have real data to make probability estimates and then you will not get as good risk estimate as you might wish.

However, in FAIR and similar frameworks you get help to elicit expert knowledge and transform it into a risk estimation. And the validity of this risk estimation is of course related to the validity of the expert knowledge: If you put garbage in, you get garbage out.

But, I think you are wrong when you are saying that the input to FAIR is arbitrary. Of course, if used incorrectly, the input can be arbitrary.

My question is: why would anybody that seriously wants to use FAIR make "arbitrary" input? Why not make "guesses" that are the best according to your knowledge? Then, based on the input and its modeling assumptions, FAIR will output the best possible risk estimation (at least if you believe in Bayesian statistics and decision theory..).

This means that you cannot make any better risk estimation based on the knowledge you have given as input without changing the FAIR model or adding more input.

So if you have to make decision that is the best according to you knowledge, then FAIR might work well.