Training Course on Steering an Expert Knowledge Elicitation

Knowledge Elicitation Training and Dissemination Report

This paper focuses on a participatory knowledge elicitation process (KnETs) to explore decision-making criteria regarding ‘soft’ techniques for flood risk management in the Ukrainian Tisza river basin. Communities in this region are faced with frequent floods and limited governmental budgets to cope with flood impacts. To identify the potential for soft flood protection measures as opposed to t...

Expert Commentary on Videoed Expertise (EXCOVE): An approach to knowledge elicitation in little-understood, complex domains

Regression with n→1 by Expert Knowledge Elicitation

We consider regression under the “extremely small n large p” condition. In particular, we focus on problems with so small sample sizes n compared to the dimensionality p, even n → 1, that predictors cannot be estimated without prior knowledge. Furthermore, we assume all prior knowledge that can be automatically extracted from databases has already been taken into account. This setup occurs in p...

Expert knowledge elicitation to improve formal and mental models

Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. Descriptions of these relationships are not generally available from traditional data sources but are stored in the mental models of experts. Often the knowledge is not explicit but tacit, so it is dicult to describe, examine, and use. Consequently, improvem...

Expert Knowledge Elicitation to Improve Mental and Formal Models

Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. For example, product development is guided by expert knowledge including critical process relationships which are dynamic, biased by individual perspectives and goals, conditioned by experience, aggregate many system components and relationships and are often...