Table of Contents Author Guidelines Submit a Manuscript
Advances in Decision Sciences
Volume 2014 (2014), Article ID 215218, 10 pages
Research Article

Understanding Decision Making through Complexity in Professional Networks

Complex Systems Research Group, Project Management Program, The University of Sydney, Camperdown, NSW 2006, Australia

Received 29 May 2014; Accepted 13 November 2014; Published 11 December 2014

Academic Editor: Mahyar A. Amouzegar

Copyright © 2014 Kon Shing Kenneth Chung. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The attitudes of general practitioners (GP) play an influential role in their decision making about patient treatment and care. Considering the GP-patient encounter as a complex system, the interactions between the GP and their personal network of peers give rise to “aggregate complexity,” which in turn influences the GP’s decisions about patient treatment. This study models aggregate complexity and its influence in decision making in primary care through the use of social network metrics. Professional network and attitudinal data on decision making responsibility from 107 rural GPs were analysed. Social network measures of “density” and “inclusiveness” were used for computing the “interrelatedness” of components within such a “complex system.” The “number of components” and “degree of interrelatedness” were used to determine the complexity profiles, which was then used to associate with responsibility in decision making for each GP. GPs in simple profiles (i.e., with low components and interactions) in contrast to those in nonsimple profiles, indicate a higher responsibility for the decisions they make in medical care. This study suggests that social networks-based complexity profiles are useful for understanding decision making in primary care as it accounts for the role of influence through the professional networks of GPs.