scholarly journals Expert Knowledge Elicitation on Indicators for Success of Stunning and Killing

2013 ◽  
Vol 10 (12) ◽  
Author(s):  
Piet Sellke
Keyword(s):  
Expert Knowledge ◽  
Knowledge Elicitation

scholarly journals An FDA-Based Approach for Clustering Elicited Expert Knowledge

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 184-204
Author(s):  
Carlos Barrera-Causil ◽  
Juan Carlos Correa ◽  
Andrew Zamecnik ◽  
Francisco Torres-Avilés ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Functional Data ◽  
Expert Knowledge ◽  
Probability Distributions ◽  
Knowledge Elicitation ◽  
Parallel Analysis ◽  
Data Sets ◽  
Dimensional Problem ◽  
Prior Distributions ◽  
Infinite Dimensional ◽  
Finite Dimensional

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

Investigating the role of Non-Technical Skills in Railway Traffic Operations through Expert Knowledge Elicitation

2021 ◽  
Vol 65 (1) ◽  
pp. 262-266
Author(s):  
Iga Jarosz* ◽  
Julia Lo ◽  
Jan Lijs
Keyword(s):  
Traffic Control ◽  
Expert Knowledge ◽  
Skill Training ◽  
Technical Skills ◽  
Knowledge Elicitation ◽  
Protective Function ◽  
Nontechnical Skills ◽  
Railway Traffic ◽  
The One ◽  
Expert Classification

Many high-risk industries identify non-technical skills as safety-critical abilities of the operational staff that have a protective function against human fallibility. Based on an established non-technical skills classification system, methods for expert knowledge elicitation were used to describe non-technical skills in the specific context of train traffic control in the Netherlands. The findings offer insights regarding the skill importance for good operational outcomes, skill difficulty, categorization, and attitudes based on subject matter experts’ opinions. Substantial overlap between the employed non-technical skills framework and the observed expert classification was found, which might indicate that the experts utilize a mental model of nontechnical skills similar to the one used. Furthermore, considerations concerning the organizational culture and the attitudes towards change provide a promising outlook when introducing novel solutions to non-technical skill training and assessment.

scholarly journals Eliciting experts’ knowledge in emergency response organizations

2014 ◽  
Vol 3 (2) ◽  
pp. 118-130 ◽  
Author(s):  
Justin Okechukwu Okoli ◽  
Gordon Weller ◽  
John Watt
Keyword(s):  
Tacit Knowledge ◽  
Expert Knowledge ◽  
Knowledge Elicitation ◽  
Content Type ◽  
Elicitation Study ◽  
Structured Knowledge ◽  
Fire Stations ◽  
The Uk ◽  
Practical Implications ◽  
Fire Ground

Purpose – Experienced fire ground commanders are known to make decisions in time-pressured and dynamic environments. The purpose of this paper is to report some of the tacit knowledge and skills expert firefighters use in performing complex fire ground tasks. Design/methodology/approach – This study utilized a structured knowledge elicitation tool, known as the critical decision method (CDM), to elicit expert knowledge. Totally, 17 experienced firefighters were interviewed in-depth using a semi-structured CDM interview protocol. The CDM protocol was analysed using the emergent themes analysis approach. Findings – Findings from the CDM protocol reveal both the salient cues sought, which the authors termed critical cue inventory (CCI), and the goals pursued by the fire ground commanders at each decision point. The CCI is categorized into five classes based on the type of information each cue generates to the incident commanders. Practical implications – Since the CDM is a useful tool for identifying training needs, this study discussed the practical implications for transferring experts’ knowledge to novice firefighters. Originality/value – Although many authors recognize that experts perform exceptionally well in their domains of practice, the difficulty still lies in getting a structured method for unmasking experts’ tacit knowledge. This paper is therefore relevant as it presents useful findings following a naturalistic knowledge elicitation study that was conducted across different fire stations in the UK and Nigeria.

The Cost of Knowledge Recovery: A Challenge for the Application of Concept Mapping

2007 ◽  
Vol 51 (4) ◽  
pp. 328-331
Author(s):  
Robert R. Hoffman ◽  
Paul J. Feltovich ◽  
David W. Eccles
Keyword(s):  
Concept Maps ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Knowledge Elicitation ◽  
Terrain Analysis ◽  
Aerial Photos ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Rock Types ◽  
The Cost

Whereas knowledge management relies on processes of knowledge elicitation, there is also a process in which knowledge is “recovered,” typically from archived documents. We conducted a knowledge recovery (KR) effort, going from documents to a structured set of propositions concerning expert knowledge about terrain analysis, discussing landforms, soils, rock types, etc. Assertions and feature associations were recast as over 3,000 propositions. When contrasted with results from previous evaluations of methods of knowledge elicitation, KR was costly in terms of time and effort, suggesting that knowledge-based organizations should make knowledge capture an on-going aspect of work, rather than finding themselves in the “catch-up mode” to recover lost expertise. For both knowledge elicitation and recovery, the knowledge has to be represented in a form that is usable and useful (e.g., instantiation in knowledge bases). We created from the propositions a navigable knowledge model based on over 150 Concept Maps, which were hyperlinked together and to dozens of resources (aerial photos, maps, diagrams, etc.). Such knowledge models are intended to make the “expertise of the past” more useful and usable in training and in performance support.

scholarly journals Expert knowledge elicitation on African Swine Fever and outdoor farming of pigs

2021 ◽  
Vol 18 (6) ◽  
Author(s):  
Andy Hart ◽  
Gene Rowe ◽  
Fergus Bolger ◽  
Abigail Colson
Keyword(s):  
Expert Knowledge ◽  
African Swine Fever ◽  
Knowledge Elicitation

Expert knowledge elicitation for decision-making in home energy retrofits

2014 ◽  
Vol 32 (5) ◽  
pp. 377-395 ◽  
Author(s):  
Daniel Yaw Addai Duah ◽  
Kevin Ford ◽  
Matt Syal
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Knowledge Elicitation ◽  
Content Type ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Intelligent Decision Support

Purpose – The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges. Design/methodology/approach – Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system. Findings – A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system. Research limitations/implications – The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge. Originality/value – No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

Sign in / Sign up

Export Citation Format

Share Document