During the last decade, the computational paradigms known as inflzcence diagrams
and belief networks have become to dominate the diagnostic expert systems field.
Using elaborate collections of nodes and arcs, these representations describe how propositions
of interest interact with each other through a variety of causal and predictive links.
The links are parameterized with inexact degrees of support, typically expressed as subjective
conditional probabilities or likelihood ratios. To date, most of the research in this
area has focused on developing efficient belief-revision calculi to support decision making
under uncertainty. Taking a different perspective, this paper focuses on the inputs of these
calculi, i.e. on the human-supplied degrees of support which provide the currency of the
belief revision process. Traditional methods for eliciting subjective probability functions
are of little use in rule-based settings, where propositions of interest represent causally related
and mostly discrete random variables. We describe ratio-scale and graphical methods
for (i) eliciting degrees of support from human experts in a credible manner, and (ii) transforming
them into the conditional probabilities and likelihood-ratios required by standard
belief revision algorithms. As a secondary contribution, the paper offers a new graphical
justification to eigenvector techniques for smoothing subjective answers to pair-wise
elicitation questions.Information Systems Working Papers Serie