Causal Loop Diagram Aggregation Towards Model Completeness
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Causal Loop Diagram Aggregation Towards Model Completeness Emily Ryan 1
1
& Matthew Pepper & Albert Munoz
1
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Experienced system dynamicists commonly conceptualise causal relationships and feedback loops using Causal Loop Diagrams (CLDs). In adhering to best practice, multiple data collection activities may be required (e.g. multiple group model building sessions), resulting in multiple CLDs. To achieve covariation that correctly attributes cause and effect from multiple data sets, aggregation of CLDs may be necessary. Such aggregation must adequately account for attribution variations across constructed CLDs to produce a coherent view of a phenomenon of interest in a ‘complete’ model. Discourse concerning model completeness should account for the potential for method bias. The data collection method chosen for CLD development will influence the ability to create a model that is fit for the purposes of the study and influence the likelihood of achieving model completeness. So too does the method chosen for model aggregation. Little processual guidance exists on a method for data aggregation in system dynamics studies. This paper examines three data aggregation approaches, based on existing qualitative analysis methods, to determine the suitability of each method. The approaches considered include triangulation, includes all data in the aggregation process; grounded theory, bases aggregation on frequency of occurrence; and synthesis, extends aggregation to include variables based on magnitude of occurrence. Comments are made regarding the relevance of each method for different study types, with final remarks reiterating the consideration of equifinality and multifinality in research and their impact on method selection. This paper enhances the rigour of research aiming at facilitating greater success in studies utilising CLDs. Keywords Casual loop diagram . Aggregation . Model completeness . Synthesis
* Emily Ryan [email protected]
1
University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Systemic Practice and Action Research
Introduction System dynamics studies seek to generate a holistic understanding of the interactions among system components and their environment. Causal Loop Diagrams (CLDs) are used to provide conceptual insight into the causal relationships within a system under examination (Vennix et al. 1990). Despite identified issues with CLDs (Richardson 1986, 1997), they have remained a popular technique for system dynamicists and a useful way to complement research contributions. Research involving CLDs often focus on engaging participants from multiple areas of the system under investigation (see Cavana and Mares 2004; Lane 1992; Laurenti et al. 2014). Consensus among group members, referring to acceptance by all participants, requires that each individual be given opportunity to contribute during discussion (Korsgaard et al. 1995; Vennix 1996). Amalgamation of multiple perspectives is key for
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