Graphical models and the challenge of evidence-based practice in development and sustainability

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dc.contributor.authorCalder, Ryan S. D.en
dc.contributor.authorAlatorre, Andreaen
dc.contributor.authorMarx, Rebecca S.en
dc.contributor.authorMallampalli, Varunen
dc.contributor.authorMason, Sara A.en
dc.contributor.authorOlander, Lydia P.en
dc.contributor.authorJeuland, Marcen
dc.contributor.authorBorsuk, Mark E.en
dc.date.accessioned2020-10-13T21:11:34Zen
dc.date.available2020-10-13T21:11:34Zen
dc.date.issued2020-08-01en
dc.date.updated2020-10-13T21:11:32Zen
dc.description.abstractGovernments and social benefit organizations are expected to consider evidence in decision-making. In development and sustainability, evidence spans disciplines and methodological traditions and is often inconclusive. Graphical models are widely promoted to organize interdisciplinary evidence and improve decision-making by considering mediating variables. However, the reproducibility, objectivity and benefits for decision-making of graphical models have not been studied. We evaluate these considerations in the setting of energy services in the developing world, a contemporary development and sustainability imperative. We develop a database of relevant causal relations (313 concepts, 1337 relationships) asserted in the literature (561 peer-reviewed articles). We demonstrate that high-level relationships of interest to practitioners feature less consistent evidence than the causal relationships that underpin them, supporting increased use of problem decomposition through graphical modeling approaches. However, adding such detail increases complexity exponentially, introducing a hazard of overparameterization if evidence is not available to match the level of mechanistic detail.en
dc.description.versionPublished versionen
dc.format.extent8 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 104734 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.envsoft.2020.104734en
dc.identifier.eissn1873-6726en
dc.identifier.issn1364-8152en
dc.identifier.orcidCalder, Ryan [0000-0001-5618-9840]en
dc.identifier.urihttp://hdl.handle.net/10919/100476en
dc.identifier.volume130en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectTechnologyen
dc.subjectLife Sciences & Biomedicineen
dc.subjectComputer Science, Interdisciplinary Applicationsen
dc.subjectEngineering, Environmentalen
dc.subjectEnvironmental Sciencesen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectEnvironmental Sciences & Ecologyen
dc.subjectResults chainen
dc.subjectBayesian networken
dc.subjectLogic modelen
dc.subjectEvidence assessmenten
dc.subjectINTERNATIONAL DEVELOPMENTen
dc.subjectSYSTEMATIC REVIEWSen
dc.subjectCRITIQUEen
dc.subjectEnvironmental Engineeringen
dc.titleGraphical models and the challenge of evidence-based practice in development and sustainabilityen
dc.title.serialEnvironmental Modelling & Softwareen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Tech/Veterinary Medicineen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Veterinary Medicine/Population Health Sciencesen
pubs.organisational-group/Virginia Tech/Veterinary Medicine/CVM T&R Facultyen
pubs.organisational-group/Virginia Techen

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