Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Abstract

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.

Evaluation of complex community‐based childhood obesity prevention interventions

“…active transport, bicycle usage, green recreation, healthy food choices) to primary outcomes (i.e. BMI) . Traditional process evaluation (i.e.…”

A Method for Simplification of Complex Group Causal Loop Diagrams Based on Endogenisation, Encapsulation and Order-Oriented Reduction

“…Last, multi-input and multi-output (MIMO) variables are those which shape the system structure and significantly influence system behaviour. Thus, the reduction needs to be performed from the simplest model elements (pure inputs, sometimes denoted as out-degrees, and outputs, sometimes detonated as in-degrees [21]) to more complicated single-input single-output (SISO) variables, single-input double-output and double-input single-output (SIDO and DISO, respectively) variables, double-input double-output (DIDO) variables, etc. Apparently, as the complexity grows, proportionally the order of variables included in the model does.…”

“…In fact, the method as a whole is centrality-oriented as it primarily supports MIMO variables while in-degree, out-degree, and betweenness (mediator) variables are suppressed [21]. Three activities presented above are ordered here according to extent of their damage on systemic nature of described model caused by the simplification.…”

“…The complexity of a causal loop diagram can be expressed in many ways. For instance, McGlashan et al [21] apply indicators used in the network analysis domain. They describe their diagrams by metrics such as the average path length in the network (in how many causal paths variables are able to reach each other on average); the most distant variables (identification of the shortest path between variable), which determines the CLD's diameter; or the network modularity that indicates the presence of structural clusters of variables in the network.…”

“…As time goes by, static systems represent only snapshots of dynamic behaviours of systems-of-interest. The complexity of dynamic systems is rooted in many system attributes ranging from nonlinearity to relationships between multiple causes and their effects [21].…”