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SHELF: The Sheffield Elicitation Framework

In: Elicitation

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  • John Paul Gosling

    (University of Leeds)

Abstract

The Sheffield elicitation framework is an expert knowledge elicitation framework that has been devised over a number of years and many substantial expert knowledge elicitation exercises to give a transparent and reliable way of collecting expert opinions. The framework is based on the principles of behavioural aggregation where a facilitator-guided group interact and share information to arrive at a consensus. It was originally designed for helping to elicit judgements about single uncertain variables, but, in recent years, the framework and the associated software implementations have been extended to accommodate judgements about more complex multidimensional variables and geographically-dispersed experts. In this chapter, we discuss the aims and foundations of the framework, its extensions and its notable applications.

Suggested Citation

  • John Paul Gosling, 2018. "SHELF: The Sheffield Elicitation Framework," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 61-93, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65052-4_4
    DOI: 10.1007/978-3-319-65052-4_4
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    Cited by:

    1. Claire Copeland & Britta Turner & Gareth Powells & Kevin Wilson, 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures," Energies, MDPI, vol. 15(15), pages 1-21, July.
    2. Raices Cruz, Ivette & Lindström, Johan & Troffaes, Matthias C.M. & Sahlin, Ullrika, 2022. "Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    3. Danila Azzolina & Paola Berchialla & Silvia Bressan & Liviana Da Dalt & Dario Gregori & Ileana Baldi, 2022. "A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    4. Christopher J. Cadham & Marie Knoll & Luz María Sánchez-Romero & K. Michael Cummings & Clifford E. Douglas & Alex Liber & David Mendez & Rafael Meza & Ritesh Mistry & Aylin Sertkaya & Nargiz Travis , 2022. "The Use of Expert Elicitation among Computational Modeling Studies in Health Research: A Systematic Review," Medical Decision Making, , vol. 42(5), pages 684-703, July.
    5. Jeroen P. Jansen & Thomas A. Trikalinos & Kathryn A. Phillips, 2022. "Assessments of the Value of New Interventions Should Include Health Equity Impact," PharmacoEconomics, Springer, vol. 40(5), pages 489-495, May.
    6. Alice Morgan & Sally Hartmanis & Emmanuel Tsochatzis & Philip N. Newsome & Stephen D. Ryder & Rachel Elliott & Lefteris Floros & Richard Hall & Victoria Higgins & George Stanley & Sandrine Cure & Shar, 2021. "Disease burden and economic impact of diagnosed non-alcoholic steatohepatitis (NASH) in the United Kingdom (UK) in 2018," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(4), pages 505-518, June.
    7. Perepolkin, Dmytro & Lindsröm, Erik & Sahlin, Ullrika, 2023. "Quantile-parameterized distributions for expert knowledge elicitation," OSF Preprints tq3an, Center for Open Science.
    8. Fadlalla G. Elfadaly & Paul H. Garthwaite, 2020. "On quantifying expert opinion about multinomial models that contain covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 959-981, June.
    9. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
    10. Cameron J. Williams & Kevin J. Wilson & Nina Wilson, 2021. "A comparison of prior elicitation aggregation using the classical method and SHELF," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 920-940, July.

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