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An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic-Hazard Analysis

2013; Seismological Society of America; Volume: 103; Issue: 5 Linguagem: Inglês

10.1785/0120130026

1943-3573

Armin Runge, Frank Scherbaum, Andrew Curtis, Carsten Riggelsen,

Reservoir Engineering and Simulation Methods

Research Article| October 01, 2013 An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic‐Hazard Analysis Antonia K. Runge; Antonia K. Runge Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyAntonia.Runge@geo.unipotsdam.deFrank.Scherbaum@geo.uni-potsdam.de Search for other works by this author on: GSW Google Scholar Frank Scherbaum; Frank Scherbaum Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyAntonia.Runge@geo.unipotsdam.deFrank.Scherbaum@geo.uni-potsdam.de Search for other works by this author on: GSW Google Scholar Andrew Curtis; Andrew Curtis School of GeoSciences, University of Edinburgh, Grant Institute, The King’s Buildings, West Mains Road, Edinburgh EH9 3JW, UKAndrew.Curtis@ed.ac.uk Search for other works by this author on: GSW Google Scholar Carsten Riggelsen Carsten Riggelsen Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyCarsten.Riggelsen@geo.uni-potsdam.de Search for other works by this author on: GSW Google Scholar Author and Article Information Antonia K. Runge Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyAntonia.Runge@geo.unipotsdam.deFrank.Scherbaum@geo.uni-potsdam.de Frank Scherbaum Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyAntonia.Runge@geo.unipotsdam.deFrank.Scherbaum@geo.uni-potsdam.de Andrew Curtis School of GeoSciences, University of Edinburgh, Grant Institute, The King’s Buildings, West Mains Road, Edinburgh EH9 3JW, UKAndrew.Curtis@ed.ac.uk Carsten Riggelsen Institute of Earth and Environmental Science, University of Potsdam, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, GermanyCarsten.Riggelsen@geo.uni-potsdam.de Publisher: Seismological Society of America First Online: 14 Jul 2017 Online ISSN: 1943-3573 Print ISSN: 0037-1106 Bulletin of the Seismological Society of America (2013) 103 (5): 2862–2874. https://doi.org/10.1785/0120130026 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Antonia K. Runge, Frank Scherbaum, Andrew Curtis, Carsten Riggelsen; An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic‐Hazard Analysis. Bulletin of the Seismological Society of America 2013;; 103 (5): 2862–2874. doi: https://doi.org/10.1785/0120130026 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract In probabilistic seismic‐hazard analysis, epistemic uncertainties are commonly treated within a logic‐tree framework in which the branch weights express the degree of belief of an expert in a set of models. For the calculation of the distribution of hazard curves, these branch weights represent subjective probabilities. A major challenge for experts is to provide logically consistent weight estimates (in the sense of Kolmogorovs axioms), to be aware of the multitude of heuristics, and to minimize the biases which affect human judgment under uncertainty. We introduce a platform‐independent, interactive program enabling us to quantify, elicit, and transfer expert knowledge into a set of subjective probabilities by applying experimental design theory, following the approach of Curtis and Wood (2004). Instead of determining the set of probabilities for all models in a single step, the computer‐driven elicitation process is performed as a sequence of evaluations of relative weights for small subsets of models. From these, the probabilities for the whole model set are determined as a solution of an optimization problem. The result of this process is a set of logically consistent probabilities together with a measure of confidence determined from the amount of conflicting information which is provided by the expert during the relative weighting process. We experiment with different scenarios simulating likely expert behaviors in the context of knowledge elicitation and show the impact this has on the results. The overall aim is to provide a smart elicitation technique, and our findings serve as a guide for practical applications.Online Material: Interactive software for the elicitation of expert knowledge. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.