Use of expert knowledge to anticipate the future : issues, analysis and directions
Bolger, Fergus and Wright, George (2017) Use of expert knowledge to anticipate the future : issues, analysis and directions. International Journal of Forecasting, 33 (1). pp. 230-243. ISSN 0169-2070 (https://doi.org/10.1016/j.ijforecast.2016.11.001)
Preview |
Text.
Filename: Bolger_Wright_IJF_2016_Use_of_expert_knowledge_to_anticipate_the_future.pdf
Accepted Author Manuscript License: Download (675kB)| Preview |
Abstract
Unless the anticipation problem is routine and short-term, and objective data are plentiful, expert judgment will be needed. Risk assessment is analogous to anticipation of the future in that models need to be developed and applied to data. Since objective data are often scanty, expert knowledge elicitation (EKE) techniques have been developed for risk assessment that allow model development and parametrization using expert judgments with minimal cognitive and social biases. Here, we conceptualize how EKE can be developed and applied to support anticipation of the future. Accordingly, we first define EKE as an entire process, that involves considering experts as a source of data, and that comprises various methods for ensuring the quality of this data, including – selecting the best experts, training experts in normative aspects of anticipation, and combining judgments of several experts – as well as eliciting unbiased estimates and constructs from experts. We detail aspects of the papers that constitute the Special Issue and analyse these in terms of the stages within the EKE future-anticipation process that they address. We identify the remaining gaps in our knowledge. Our conceptualization of EKE to support anticipation of the future is compared and contrasted with the extant research effort into judgmental forecasting.
ORCID iDs
Bolger, Fergus and Wright, George ORCID: https://orcid.org/0000-0002-4350-7800;-
-
Item type: Article ID code: 59062 Dates: DateEvent31 March 2017Published10 December 2016Published Online19 September 2016AcceptedSubjects: Science > Mathematics > Probabilities. Mathematical statistics
Social Sciences > Industries. Land use. Labor > Risk ManagementDepartment: Strathclyde Business School > Strategy and Organisation Depositing user: Pure Administrator Date deposited: 12 Dec 2016 10:04 Last modified: 21 Nov 2024 01:12 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/59062