Historically, the evolution of expert systems has been driven by scientifically based fields such as medicine, geology, and computer engineering. More recently, expert system developers have turned their attention to the highly judgmental decision tasks found in business and finance. We introduce the corporate assessment problem, point out the limitations of current expert system approaches to the solution to this problem, and suggest that a more fundamental approach based on recent work in qualitative physics might be fruitful.

Most high-performance expert systems rely primarily porations, describe the reasoning styles currently used by on an ability to represent surface knowledge about associations people, and show how some of these assessments can be between observable evidence or data, on the one addressed by extending existing AI techniques. Although the present generation of practical systems qualitative causal models in an expert system-remains a shows that this architectural style can be pushed speculative subject. The larger firms are subject to intense captured in the second model would be selected to complement scrutiny by armies of financial analysts, and even the the associational knowledge represented in the first smaller corporations have creditors of various sorts who module. The details of Simulation models have been especially attractive the procedures used to make assessments vary according choices for the complementary representation because of to the specific objective of the analyst. It might be that an the causal relations embedded in them (Brown & Burton, equity investment is under consideration, that a loan request 1975; Cuena, 1983).