Modeling expert knowledge using "situation-action" rules is not always feasible in knowledge
intensive domains involving volatile knowledge (e.g., trading). The explosive search
space involved in such domains and its dynamic nature make it extremely difficult to setup
a rule base and keep it accurate. An alternative approach suggests that in some domains
many of the rules expert use can be derived by reasoning from "first-principles". That approach
entails modeling experts' deep knowledge, and emulating reasoning processes with
deep knowledge that allow experts to derive many of the rules they use and justify them.
This paper discusses the design and implementation of an object-oriented representation
for the deep knowledge traders utilize in a business domain called hedging, which is knowledge
intensive and involves volatile knowledge. It illustrates how deep knowledge modeled
using that representation is used to support reasoning from first-principles. The paper also
analyzes features of that representation that we have found to be extremely beneficial in
the development of a knowledge-based system called INTELLIGENT-HEDGER. Based on
our experience we feel that, with minor modifications, this representation can be used in
other managerial domains involving financial reasoning.Information Systems Working Papers Serie