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• W3193430237 abstract "Cross-impact balance (CIB) analysis leverages expert knowledge pertaining to the nature and strength of relationships between components of a system to identify the most plausible future 'scenarios' of the system. These scenarios, also referred to as 'storylines', provide qualitative insights into how the state of one factor can either promote or restrict the future state of one or multiple other factors in the system. This paper presents a novel, visually oriented questionnaire developed to elicit expert knowledge about the relationships between key factors in a system, for the purpose of CIB analysis. The questionnaire requires experts to make selections from a series of standardized cause-effect graphical profiles that depict a range of linear and non-linear relationships between factor pairs. The questionnaire and the process of translating the graphical selections into data that can be used for CIB analysis is described using an applied example which focuses on urban health in Latin American cities." @default.
• W3193430237 created "2021-08-30" @default.
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• W3193430237 date "2021-01-01" @default.
• W3193430237 modified "2023-09-27" @default.
• W3193430237 title "Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis" @default.
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• W3193430237 doi "https://doi.org/10.1016/j.mex.2021.101492" @default.
• W3193430237 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7611690" @default.
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• W3193430237 hasPublicationYear "2021" @default.
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