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

Jaimie Mcglashan,Doug Creighton,Steven Allender,Kayla De La Haye,Michael Johnstone,Houbing Song

doi:10.1371/journal.pone.0165459

Jaimie Mcglashan, Doug Creighton + Show 4 more

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https://doi.org/10.1371/journal.pone.0165459

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Journal: PloS one	Publication Date: Oct 27, 2016
Citations: 77	License type: CC BY 4.0

Affiliation: Deakin University, University of Southern California

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.

Highlights

Complex problems can be difficult to understand and resolve due to the relationships between their multiple dynamic causes
The causal loop diagrams (CLDs) describing childhood obesity was developed via group model building across four workshops in 2014
Small world and scale free networks, which are observed in many empirical networks, have properties that are well known to influence the function and resilience of the network, and provide useful insights into the function of the CLD

Summary

Introduction

Complex problems can be difficult to understand and resolve due to the relationships between their multiple dynamic causes. It has been suggested that any intervention seeking to tackle complexity would be better served if a shared understanding of the complexity was developed to support intervention design, implementation and evaluation [3]. Among the numerous approaches available to understand and share knowledge of complexity [4], systems science methods appear the most promising [5]. System science techniques range in their utility for community engagement and collect broad views of complexity from fully engaged, process driven methods to small group highly quantitative approaches designed primarily to generate mathematical simulation [6,7,8].

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