A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition

TL;DR

This paper introduces a decision-theoretic approach to representing partial confidence in beliefs and preferences, aiding knowledge acquisition by focusing attention on areas needing refinement.

Contribution

It presents a novel representation of partial confidence aligned with decision theory, enabling targeted refinement during knowledge acquisition.

Findings

Model improves decision quality by accounting for partial confidence.

Decision-analytic approach balances modeling benefits and costs.

Method guides experts to focus on uncertain parts of the model.

Abstract

We present a representation of partial confidence in belief and preference that is consistent with the tenets of decision-theory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the assessment may improve decisions to which it is relevant. We show how a traditional decision-analytic approach can be used to balance the benefits of additional modeling with associated costs. The approach can be used during knowledge acquisition to focus the attention of a knowledge engineer or expert on parts of a decision model that deserve additional refinement.