Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis

Ivana Stankov, Andres F. Useche, Jose D. Meisel, Felipe Montes, Lidia MO Morais, Amelia AL Friche, Brent A. Langellier, Peter Hovmand, Olga L. Sarmiento, Ross A. Hammond, Ana V. Diez Roux

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

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. • A questionnaire featuring a set of standardized cause-effect profiles was developed. • Cause-effect profiles were used to elicit information about the strength of linear and non-linear bivariate relationships. • The questionnaire represents an intuitive visual means for collecting data required for the conduct of CIB analysis.

Idioma originalInglés
Número de artículo101492
PublicaciónMethodsX
Volumen8
DOI
EstadoPublicada - ago 2021

Huella

Profundice en los temas de investigación de 'Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis'. En conjunto forman una huella única.

Citar esto