Place and Health as Complex Systems: A Case Study and Empirical Test

Back in the spring and summer of 2010 I posted a series of discussions about the need for complexity scientists to do a better job of comprehensively testing the empirical utility of their definitions--see, for example, one of the posting by clicking here.  My main argument was that:

1. Most complexity science today explores only specific aspects of complex systems, such as emergence or network properties.
2. While only specific aspects are explored, these same scientists assume the full definition upon which they rely to be true in terms of their topic of study, but without empirical test.
3. The testing I recommend is not about determining if a topic is a complex system, which is useless as most things are complex systems.  Instead, testing should focus on the empirical and theoretical utility of the definition used.  In other words, does the definition yield new insights that could not otherwise have been obtained?
4.  The testing I recommend should also link complexity method with definition.  In other words, scientists need to explore how complexity methods (in particular, computational modeling, case-based modeling, qualitative method, etc) help to determine/demonstrate the empirical utility of defining a topic as a complex system.

At the end of my series of posts I noted that my colleagues and I were working on an article to address this issue, as pertains to the study of community health and school systems.

Well, a year and a half later, our study on community health is done--CLICK HERE TO DOWNLOAD IT.  Here is the abstract:

Abstract: Over the last decade, scholars have developed a complexities of place (COP) approach to the study of place and health. According to COP, the problem with conventional research is that it lacks effective theories and methods to model the complexities of communities and so forth, given that places exhibit nine essential "complex system" characteristics: they are (1) causally complex, (2)  self-organizing and emergent, (3) nodes within a larger network, (4) dynamic and evolving, (5) nonlinear, (6) historical, (7) open-ended with fuzzy boundaries, (8) critically conflicted and negotiated, and (9) agent-based.While promising, the problem with the COP approach, however, is that its definition remains systematically untested and its recommended complexity methods (e.g., network analysis, agent-based modeling) remain underused.  The current article, which is based on a previous abbreviated study and its ”sprawl and community-level health” database, tests the empirical utility of the COP approach. In our abbreviated study, we only tested characteristics 4 and 9. The current article conducts an exhaustive test of all nine characteristics and suggested complexity methods. 

Method: To conduct our test we made two important advances: First, we developed and applied the Definitional Test of Complex Systems (DTCS) to a case study on sprawl—a ”complex systems” problem—to examine, in litmus test fashion, the empirical validity of the COP’s 9-characteristic definition. Second, we used the SACS Toolkit, a case-based modeling technique for studying complex system that employs a variety of complexity methods. For our case study we examined a network of 20 communities (located in Summit County, Ohio USA) negatively impacted by sprawl. Our database was partitioned from the Summit 2010: Quality of Life Project. 

Results: Overall, the DTCS found the COP’s 9-characteristic definition to be empirically valid. The employment of the SACS Toolkit supports also the empirical novelty and utility of complexity methods. Nonetheless, minor issues remain, such as a need to define health and health care in complex systems terms.

Conclusions: The COP approach seems to hold real empirical promise as a useful way to address many of the challenges that conventional public health research seems unable to solve; in particular, modeling the complex evolution and dynamics of places and addressing the causal interplay between compositional and contextual factors and their impact on community-level health outcomes.

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