Project Details
Description
This project will combine logic and probability theory to develop new
methods and decision aids for causal reasoning. We will use a new
axiomatization of the classical idea of a probability tree, which goes well
beyond the current paradigm of Bayes nets in allowing modular
representation of probabilistic information. Financial statement auditing
will be used as a test bed for the methods, which will ultimately be
applicable to many domains where expert systems are used.
The goal of an audit is the expression of an opinion on whether a set of
financial statements presents a business's financial position and
operations fairly in all material respects, in accordance with generally
accepted accounting principles. In reaching this opinion, the auditor may
need to consider a variety of subsidiary questions: whether items such as
the value of inventory are materially correct; whether internal control is
operating effectively; whether the business can continue as a going
concern, etc. The evidence bearing on these questions evidence is usually
persuasive but not conclusive, and its assessment must be based on a causal
model of the business, its environment, and the audit.
Prior research has focused on representing and aggregating uncertainty
within a model. Although this research has met its own goals reasonably
well, the model itself is almost always either overly simplified or else
very specialized, and hence the research has not produced decision aids
with the flexibility to serve auditors going into new engagements. Such
aids would have to help auditors build causal models, a task they find
challenging.
Constructing adequate models means refining models. We always begin an
audit with a simplified model, but the audit constantly raises new issues
that must be incorporated into this model. The modularity allowed by our
approach permits this constant refinement. We will develop an inference
engine that permits such refinement, together with detailed casual models
for an illustrative group of audit problems and prototype decision aids
that may provide a test-bed for future empirical research on audit judgment.
Probabilistic models currently available to auditors are neither simple
enough to be used effectively nor complex enough to fit practical auditing
realities. This project will overcome these limitations by developing a
clear, rigorous and usable concept of refinement. When new evidence
reveals that factors previously deemed unimportant are, in fact, relevant,
they can be taken into account by refining the existing model, rather than
replacing it. This ability to reason simultaneously and consistently with
causal analyses at different levels of detail will allow the auditor to
avoid models that are hopelessly naive or impossible complex
Status | Finished |
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Effective start/end date | 7/1/99 → 12/31/02 |
Funding
- National Science Foundation: $353,114.00