Complexity and Systems Theory
- LAST REVIEWED: 15 June 2015
- LAST MODIFIED: 25 October 2012
- DOI: 10.1093/obo/9780199756797-0049
- LAST REVIEWED: 15 June 2015
- LAST MODIFIED: 25 October 2012
- DOI: 10.1093/obo/9780199756797-0049
Public health embraces a holistic, “cell-to-society” approach to understanding both the direct and underlying causes of disease and the conditions that contribute to an absence of well-being. In the early 21st century, interdisciplinary methods that address the interconnected and overlapping determinants of health are needed more than ever for dealing with modern, intractable, complex health problems, such as obesity and chronic disease. The relatively recent explosion of complexity science and systems thinking across a broad range of disciplines promises to bring new insight into the nature of these challenges while providing new methods for grappling with the characteristics specific to complex problems (such as nonlinearity, feedback loops, and their chaotic nature). There are varying definitions across these disciplines of complex system, systems approach, and even systems thinking. Some researchers have called for a common language and logic to describe systems approaches; this will likely emerge in the coming years as the many disciplines working together establish common terminology. In this bibliography, “complexity science” refers to the methodologies and tools used to understand complex problems, such as agent-based and system-dynamic modeling. “Systems thinking” is used more broadly in reference to approaches that support thinking about the system, as both the causes of a complex problem and the solutions to it will be found within the structure and function of the system. Systems thinking tends to be integrative and solution oriented when compared with traditional reductionist science, which is more linear and focused on locating the causes of a problem. Complexity science provides frameworks and methods for integrating large amounts of data to enable development of a detailed picture of the workings of a complex system. Systems thinking differs in that it does not require a detailed understanding of specific system dynamics but may provide direction based on one or more of the characteristics common to complex problems. The works in this bibliography either introduce the reader to the general principles of systems thinking and complexity science or review their application to public health concerns. Applied methods include system dynamics, network analysis, and agent-based modeling. Applications in public health include behavior change, program planning, evaluation, and knowledge exchange.
The works in this section tell the story of public health’s incorporation of novel methods and approaches drawn from complexity science and systems thinking in order to further strengthen its holistic approach. Plsek and Greenhalgh 2001 emphasizes the need for systems approaches and provides a guide to newcomers on the terminology and basic concepts required to understand complex systems. Leischow and Milstein 2006 introduces a collection of original and review material from leaders in systems thinking and modeling across a range of public health issues. Sterman 2006 offers an excellent introduction to the notion that dealing with complex problems requires a fundamental change in our mental models, whereas Trochim, et al. 2006 looks at new methods and tools for intervention. Finegood 2011 considers the application of a systems lens to the problem of obesity. Glass and McAtee 2006 reinforces the notion that we need to understand the many dimensions of the complexity inherent in public health challenges and calls for new metaphors and models to help us integrate a large amount of discipline-specific information. Although Mabry, et al. 2008 specifically addresses the work of the National Institutes of Health Office of Behavioral and Social Sciences Research, its account of the organization’s aggressive turn toward systems science speaks to the larger direction public health is taking to deal with issues associated with interdisciplinarity. Finally, Luke and Stamatakis 2012 argues that public health can build upon its already impressive adoption of system science research methods and presents case studies that demonstrate their utility for the uninitiated.
Finegood, Diane T. 2011. The complex systems science of obesity. In The Oxford handbook of the social science of obesity. Edited by J. Cawley, 208–236. New York: Oxford Univ. Press.
This chapter describes the characteristics of a complex system and uses the problem of obesity as an exemplar. The chapter also considers the usual responses to complex problems and provides two frameworks for intervention that do not depend on a detailed understanding of a system’s dynamics.
Glass, Thomas A., and Matthew J. McAtee. 2006. Behavioral science at the crossroads in public health: Extending horizons, envisioning the future. Social Science and Medicine 62.7: 1650–1671.
This paper argues that biology and individual health behaviors must be studied within their broader social and environmental contexts. The authors offer a multilevel framework for examining behavior but remain rooted in the reductionist paradigm by suggesting that working out the causes of a problem will lead to solutions. Available online for purchase or by subscription.
Leischow, Scott J., and Bobby Milstein. 2006. Systems thinking and modeling for public health practice. American Journal of Public Health 96.3: 403–405.
Addresses the questions of what systems thinking and modeling are and why they are important with respect to public health. Notes that a systems view emphasizes relationships, transcends boundaries, bridges silos, and embraces heterogeneity. Introduces other articles in the issue as examples of applying systems thinking and complexity to public health.
Luke, Douglas A., and Katherine A. Stamatakis. 2012. Systems science methods in public health: Dynamics, networks, and agents. Annual Review of Public Health 33:357–376.
Arguing for a broader inclusion of systems science study designs and analytic methods in public health training and curricula, the authors present three case studies, demonstrating their application to pressing public health issues (infectious disease, tobacco control, obesity). Available online for purchase or by subscription.
Mabry, Patricia L., Deborah H. Olster, Glen D. Morgan, and David B. Abrams. 2008. Interdisciplinarity and systems science to improve population health: A view from the NIH Office of Behavioral and Social Sciences Research. American Journal of Preventive Medicine 35.2S: S211–S224.
Overviews the four key programmatic directions (next-generation basic science, interdisciplinary research, systems science, a problem-based focus for population impact) of an organization leading the charge for bridging systems science and public health. Available online for purchase or by subscription.
Plsek, Paul E., and Trisha Greenhalgh. 2001. Complexity science: The challenge of complexity in health care. British Medical Journal 323.7313: 625–628.
Explains basic concepts for understanding complex adaptive systems within a health care context. An example of complexity facing a physician and patient runs throughout the paper and serves to make the concepts discussed more concrete.
Sterman, John D. 2006. Learning from evidence in a complex world. American Journal of Public Health 96.3: 505–514.
Describes three fundamental impediments to the goal of improving health policy: complexity, learning failures, and implementation challenges. Asserts that understanding complexity can help overcome policy resistance. Focuses on the role of feedback and time delays. Explores the use of simulations to facilitate learning through the creation of new feedback loops.
Trochim, William M., Derek A. Cabrera, Bobby Milstein, Richard S. Gallagher, and Scott J. Leischow. 2006. Practical challenges of systems thinking and modeling in public health. American Journal of Public Health 96.3: 538–546.
Gives brief overview of systems thinking, stressing that it goes beyond ecological models and the social determinants of health. Frames discussion with two organizing ideas: complexity and dynamics. Uses mechanical and biological metaphors to set context. Identifies eight challenges of systems thinking and modeling in public health; based on results of an empirical study.
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