22/01/2012
I am truly an embodied mind, a socio-biological concert of self
In my Individual and Society course I typically spend the first couple weeks (amongst other things) grounding our understanding of human symbolic interaction in a wider scientific frame, by examining the scientific 'wonder' of how life emerged and how human beings came into existence--or, at least, our best current ideas on how things happened. From my perspective, it is hard to understand social interaction without an appreciation of its connection to our biological and environmental existence and the larger and smaller eco-complex systems in which we operate.
Anyway, to prepare for these lectures I often read general summaries of the latest developments in science, which give me useful ways to frame a lot of material in a quick way that focuses on the bigger picture. In preparation, one of my favorite books is Bill Bryson's A Short History of Nearly Everything.
One of the chapters that always gets me is on the emergence of life (Ch19) and its discussion of the incredible complex and self-organizing dance done by the mind-numbingly wide number and variety of living organisms that come together to make up the human body. I so easily forget that, as human beings, we are actually a collection of millions of smaller living and nonliving forms, from amino acids and proteins to mitochondria and bacteria and so forth.
Reading this material also reminds me that our conscious, brain-based cognition--that thing that calls itself I--has a certain astigmatism. Living daily life engaged in symbolic interaction, we forget that this thing we call our self (this self-reflexive, conscious I) is actually a small part of a very complex system that is comprised of millions of living organisms which, when combined in the right way, allow us to exist as a symbol making complex living system. In other words, i forget that a person, as a distinct form of structural organization, as a distinct type of living being, emerges out of, in part, a collection of smaller living beings.
I am truly an embodied mind, a socio-biological concert of self.
05/01/2012
50 Years of Information Technology
I recently ran across an excellent online historical overview of the last 50 years of information technology that I think is very well done. It was put together by Jacinda Frost. Check it out!
OnlineITDegree: 50 Years of Information Technology.
OnlineITDegree: 50 Years of Information Technology.
Proceedings of the Complexity in Health Group
The Center for Complexity in Health announces today the launching of their new white-paper outlet, the Proceedings of the Complexity in
Health Group.
The
PCCH is an annual publication designed both to showcase and provide a
publication outlet for some of the main avenues of research being conducted in
the Complexity in Health Group, Robert S. Morrison Health and Science
Building, Kent State University at Ashtabula. These areas include
medical professionalism, community health, allostatic load, school systems,
medical learning environments and case-based modeling—all explored from a
complexity science perspective.
The
studies published in the PCHG are generally comprehensive, in-depth
explorations of a topic, meant to provide a wider and more complete empirical
and theoretical backdrop for the specific studies that scholars involved in the
Complexity in Health Group (CHG) regularly publish in various disciplinary
journals. Such an outlet as the PCHG is useful given the conventions
(e.g., page constraints and narrowness of focus) typical of most research
periodicals, which make it very difficult to publish relatively complete
statements on a topic in complex systems terms. While PCHG studies
augment, acknowledge and cite CHG work published in other venues, each PCHG
study is an original, distinct manuscript. Finally, PCHG studies are
peer-reviewed. Prior to publication each study is sent to colleagues for
review and criticism to ensure the highest quality of published proceedings
possible.
PCCH
and all of its studies are the copyright © property of the Complexity in Health Group, Kent State University at Ashtabula.
Manuscripts published in the PCHG should be cited appropriately, as in the
following example:
Castellani, B., Rajaram, R., Buckwalter,
JG., Ball, M., and Hafferty, F. 2012. “Place and Health as Complex Systems: A Case Study
and Empirical Test.” Proceedings of the Complexity in Health Group,
Kent State University at Ashtabula, 1(1):1-35.
Our
first publication is an in-depth exploration of several key issues in
complexity science and its intersection with the study of community health--CLICK HERE TO DOWNLOAD.
First, how does one determine the empirical utility of defining a community as
a complex system? What unique insights emerge that could not otherwise be
obtained? Second, how does one conduct a litmus test of one’s definition
of a community as a complex system in a systematic manner—something currently
not done in the complexity science literature? Third, how does one use
the methods and techniques of complexity science to conduct such a litmus test,
in combination with conventional methods such as statistics, qualitative method
and historical analysis? In our study we address all three questions, as
pertains to a case study on the link between sprawl and community-level health
in a Midwestern county (Summit County, Ohio) in the United States and the 20
communities of which it comprised.
Definitional Test of Complex Systems
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 argued that some sort of formal test was necessary that scholars could use to conduct such as test. Well, a year and a half later, here is our Definitional Test of Complex Systems.
The Definitional Test of Complex Systems:
The DTCS is our attempt at an exhaustive tool for determining the extent to which a complex system's definition fits a topic. The DTCS is not, however, a standardized instrument. As such, we have not normed or validated it. Instead, it is a conceptual tool meant to move scholars toward empirically-driven, synthetic definitions of complex systems. To do so, the DTCS walks scholars through a nine-question, four-step process of review, method, analysis, and results---see Table 2 above.
The DTCS does not seek to determine if a particular case fits a definition; instead, it seeks to determine if a definition fits a particular case. The challenge in the current literature is not whether places are complex systems; as it would be hard to prove them otherwise. Instead, the question is: how do we define the complexity of a topic? And, does such a definition yield new insights? Given this focus, Question 9 of the DTCS functions as its negative test, focusing on three related issues: the degree to which a definition (a) is being forced or incorrectly used; (b) is not a real empirical improvement over conventional theory or method; or (c) leads to incorrect results or to ideas already known by another name. Scholars can modify or further validate the DTCS to examine its further utility. Let us briefly review the steps of the DTCS:
STEP 1: To answer the DTCS's initial five questions, researchers must comb through their topic's literature to determine if and how it has been theorized as a complex system. If such a literature does exist, the goal is to organize the chosen definition of a complex system into its set of key characteristics: self-organizing, path dependent, nonlinear, agent-based, etc. For example, if our review of the community health science literature, we identified nine characteristics. If no such literature exists, or if the researchers choose to examine a different definition, they must explain how and why they chose their particular definition and its set of characteristics, including addressing epistemological issues related to translating or transporting the definition from one field to another.
STEP 2: Next, to answer the DTCS's sixth question, researchers must decide how they will define and measure a definition and its key characteristics. For example, does the literature conceptualize nonlinearity in metaphorical or literal terms? And, if measured literally, how will nonlinearity be operationalized? Once these decisions are made, researchers must decide which methods to use. As we have already highlighted, choosing a method is no easy task. So, scientists (particularly those in the social sciences) are faced with a major challenge: the DTCS requires them to test the validity of their definitions of a complex system, but such testing necessitate them to use new methods, which many are not equipped to use. It is because of this challenge that, for the current project, we employed the SACS Toolkit, which we discuss next. First, however, we need to address the final two steps of the DTCS.
STEP 3: Once questions 1 through 6 have been answered, the next step is to actually conduct the test. The goal here is to evaluate the empirical validity of each of a definition's characteristics, along with the definition as a whole. In other words, along with determining the validity of each characteristic, it must be determined if the characteristics fit together. Having made that point, we recognize that not all complexity theories (particularly metaphorical ones) seek to provide comprehensive definitions; opting instead to outline the conditions and challenges. Nonetheless, regardless of the definition used, its criteria need to be met.
STEP 4: Finally, with the analysis complete, researchers need to make their final assessment: in terms of the negative test found in question 9 and the null hypothesis of the DTCS, to what extent, and in what ways is (or is not) the chosen definition, along with its list of characteristics, empirically valid and theoretically valuable?
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 argued that some sort of formal test was necessary that scholars could use to conduct such as test. Well, a year and a half later, here is our Definitional Test of Complex Systems.
The Definitional Test of Complex Systems:
The DTCS is our attempt at an exhaustive tool for determining the extent to which a complex system's definition fits a topic. The DTCS is not, however, a standardized instrument. As such, we have not normed or validated it. Instead, it is a conceptual tool meant to move scholars toward empirically-driven, synthetic definitions of complex systems. To do so, the DTCS walks scholars through a nine-question, four-step process of review, method, analysis, and results---see Table 2 above.
The DTCS does not seek to determine if a particular case fits a definition; instead, it seeks to determine if a definition fits a particular case. The challenge in the current literature is not whether places are complex systems; as it would be hard to prove them otherwise. Instead, the question is: how do we define the complexity of a topic? And, does such a definition yield new insights? Given this focus, Question 9 of the DTCS functions as its negative test, focusing on three related issues: the degree to which a definition (a) is being forced or incorrectly used; (b) is not a real empirical improvement over conventional theory or method; or (c) leads to incorrect results or to ideas already known by another name. Scholars can modify or further validate the DTCS to examine its further utility. Let us briefly review the steps of the DTCS:
STEP 1: To answer the DTCS's initial five questions, researchers must comb through their topic's literature to determine if and how it has been theorized as a complex system. If such a literature does exist, the goal is to organize the chosen definition of a complex system into its set of key characteristics: self-organizing, path dependent, nonlinear, agent-based, etc. For example, if our review of the community health science literature, we identified nine characteristics. If no such literature exists, or if the researchers choose to examine a different definition, they must explain how and why they chose their particular definition and its set of characteristics, including addressing epistemological issues related to translating or transporting the definition from one field to another.
STEP 2: Next, to answer the DTCS's sixth question, researchers must decide how they will define and measure a definition and its key characteristics. For example, does the literature conceptualize nonlinearity in metaphorical or literal terms? And, if measured literally, how will nonlinearity be operationalized? Once these decisions are made, researchers must decide which methods to use. As we have already highlighted, choosing a method is no easy task. So, scientists (particularly those in the social sciences) are faced with a major challenge: the DTCS requires them to test the validity of their definitions of a complex system, but such testing necessitate them to use new methods, which many are not equipped to use. It is because of this challenge that, for the current project, we employed the SACS Toolkit, which we discuss next. First, however, we need to address the final two steps of the DTCS.
STEP 3: Once questions 1 through 6 have been answered, the next step is to actually conduct the test. The goal here is to evaluate the empirical validity of each of a definition's characteristics, along with the definition as a whole. In other words, along with determining the validity of each characteristic, it must be determined if the characteristics fit together. Having made that point, we recognize that not all complexity theories (particularly metaphorical ones) seek to provide comprehensive definitions; opting instead to outline the conditions and challenges. Nonetheless, regardless of the definition used, its criteria need to be met.
STEP 4: Finally, with the analysis complete, researchers need to make their final assessment: in terms of the negative test found in question 9 and the null hypothesis of the DTCS, to what extent, and in what ways is (or is not) the chosen definition, along with its list of characteristics, empirically valid and theoretically valuable?
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.
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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