When Should a Decision Maker Ignore the Advice of a Decision Aid?
  This paper argues that the principal difference between decision aids and
most other types of information systems is the greater reliance of decision
aids on fallible algorithms--algorithms that sometimes generate incorrect
advice. It is shown that interactive problem solving with a decision aid that
is based on a fallible algorithm can easily result in aided performance which
is poorer than unaided performance, even if the algorithm, by itself, performs
significantly better than the unaided decision maker. This suggests that unless
certain conditions are satisfied, using a decision aid as an aid is
counterproductive. Some conditions under which a decision aid is best used as
an aid are derived.
