RATIO-SCALE ELICITATION OF DEGREES OF BELIEF

Abstract

Most research on rule-based inference under uncertainty has focused on the normative validity and efficiency of various belief-update algorithms. In this paper we shift the attention to the inputs of these algorithms, namely, to the degrees of beliefs elicited from domain experts. Classical methods for eliciting continuous probability functions are of little use in a rule-based model, where propositions of interest are taken to be causally related and, typically, discrete, random variables. We take the position that the numerical encoding of degrees of belief in such propositions is somewhat analogous to the measurement of physical stimuli like brightness, weight, and distance. With that in mind, we base our elicitation techniques on statements regarding the relative likelihoods of various clues and hypotheses. We propose a formal procedure designed to (a) elicit such inputs in a credible manner, and, (b) transform them into the conditional probabilities and likelihood-ratios required by Bayesian inference systems.