  At last year?s Uncertainty in AI Conference, we reported the results of a
sensitivity analysis study of Pathfinder. Our findings were quite
unexpected-slight variations to Pathfinder?s parameters appeared to lead to
substantial degradations in system performance. A careful look at our first
analysis, together with the valuable feedback provided by the participants of
last year?s conference, led us to conduct a follow-up study. Our follow-up
differs from our initial study in two ways: (i) the probabilities 0.0 and 1.0
remained unchanged, and (ii) the variations to the probabilities that are close
to both ends (0.0 or 1.0) were less than the ones close to the middle (0.5).
The results of the follow-up study look more reasonable-slight variations to
Pathfinder?s parameters now have little effect on its performance. Taken
together, these two sets of results suggest a viable extension of a common
decision analytic sensitivity analysis to the larger, more complex settings
generally encountered in artificial intelligence.
