A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
This work addresses the challenge of efficiently refining decision models for knowledge engineers or experts, but it appears incremental as it builds on traditional decision-analytic methods.
The paper tackles the problem of representing partial confidence in belief and preference to improve decision-making by balancing the benefits of additional modeling with costs, showing how this approach can focus attention on refining decision models during knowledge acquisition.
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.