06/01/2012
Researchers in the social sciences currently employ a variety of mathematical/computational models for studying complex systems. Despite the diversity of these models, the majority can be grouped into one of three types: agent (rulebased) modeling, dynamical (equationbased) modeling and statistical (aggregatebased) modeling. The purpose of the current paper is to offer a fourth type: casebased modeling. To do so, we review the SACS Toolkit: a new method for quantitatively modeling complex social systems, based on a casebased, computational approach to data analysis. The SACS Toolkit is comprised of three main components: a theoretical blueprint of the major components of a complex system (social complexity theory); a set of casebased instructions for modeling complex systems from the ground up (assemblage); and a recommended list of casefriendly computational modeling techniques (casebased toolset). Developed as a variation on Byrne (in Sage Handbook of CaseBased Methods, pp. 260–268, 2009), the SACS Toolkit models a complex system as a set of kdimensional vectors (cases), which it compares and contrasts, and then condenses and clusters to create a lowdimensional model (map) of a complex system’s structure and dynamics over time/space. The assembled nature of the SACS Toolkit is its primary strength. While grounded in a defined mathematical framework, the SACS Toolkit is methodologically openended and therefore adaptable and amenable, allowing researchers to employ and bring together a wide variety of modeling techniques. Researchers can even develop and modify the SACS Toolkit for their own purposes. The other strength of the SACS Toolkit, which makes it a very effective technique for modeling large databases, is its ability to compress data matrices while preserving the most important aspects of a complex system’s structure and dynamics across time/space. To date, while the SACS Toolkit has been used to study several topics, a mathematical outline of its casebased approach to quantitative analysis (along with a case study) has yet to be written–hence the purpose of the current paper.
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01/01/2016
In the health informatics era, modeling longitudinal data remains problematic. The issue is method: health data are highly nonlinear and dynamic, multilevel and multidimensional, comprised of multiple major/minor trends, and causally complex—making curve fitting, modeling, and prediction difficult. The current study is fourth in a series exploring a case‐based density (CBD) approach for modeling complex trajectories, which has the following advantages: it can (1) convert databases into sets of cases (k dimensional row vectors; i.e., rows containing k elements); (2) compute the trajectory (velocity vector) for each case based on (3) a set of bio‐social variables called traces; (4) construct a theoretical map to explain these traces; (5) use vector quantization (i.e., k‐means, topographical neural nets) to longitudinally cluster case trajectories into major/minor trends; (6) employ genetic algorithms and ordinary differential equations to create a microscopic (vector field) model (the inverse problem) of these trajectories; (7) look for complex steady‐state behaviors (e.g., spiraling sources, etc) in the microscopic model; (8) draw from thermodynamics, synergetics and transport theory to translate the vector field (microscopic model) into the linear movement of macroscopic densities; (9) use the macroscopic model to simulate known and novel case‐based scenarios (the forward problem); and (10) construct multiple accounts of the data by linking the theoretical map and k dimensional profile with the macroscopic, microscopic and cluster models. Given the utility of this approach, our purpose here is to organize our method (as applied to recent research) so it can be employed by others.
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01/01/2016
Probability distributions have proven effective at modeling diversity in complex systems. The two most common are the Gaussian normal and skewed‐right. While the mechanics of the former are well‐known; the latter less so, given the significant limitations of the power‐law. Moving past the power‐law, we demonstrate that there exists, hidden‐in‐full‐view, a limiting law governing the diversity of complexity in skewed‐right systems; which can be measured using a case‐based version C c of Shannon entropy, resulting in a 60/40 rule. For our study, given the wide range of approaches to measuring complexity (i.e., descriptive, constructive, etc), we examined eight different systems, which varied significantly in scale and composition (from galaxies to genes). We found that skewed‐right complex systems obey the law of restricted diversity; that is, when plotted for a variety of natural and human‐made systems, as the diversity of complexity → ∞ (primarily in terms of the number of types; but also, secondarily, in terms of the frequency of cases) a limiting law of restricted diversity emerges, constraining the majority of cases to simpler types. Even more compelling, this limiting law obeys a scale‐free 60/40 rule: when measured using C c , 60%(or more) of the cases in these systems reside within the first 40% (or less) of the lower bound of equiprobable diversity types—with or without long‐tail and whether or not the distribution fits a power‐law. Furthermore, as an extension of the Pareto Principle, this lower bound accounts for only a small percentage of the total diversity; that is, while the top 20% of cases constitute a sizable percentage of the total diversity in a system, the bottom 60% are highly constrained. In short, as the central limit theorem governs the diversity of complexity in normal distributions, restricted diversity seems to govern the diversity of complexity in skewed‐right distributions.
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10/01/2003
The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the SelfOrganizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.
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01/01/2009
Many digital databases housed on the web today are organized in ways that are problematic for systems researchers, primarily because they are prearranged for conventional, reductionistic, linear, statisticallyaggregated re search. To make use of such data, systems researchers need an intermediary, escientific framework that can translate their digital data into a “systemsoriented” format, so that this data can be modeled and analyzed from a complex systems perspective. We have designed just such an intermediary framework, called the SACS Toolkit. The SACS Toolkit helps systems researchers translate and use digital data trapped in nonuseful formats through its unique systemsbased ontology and methodology. In the current article, we demonstrate the utility of the SACS Toolkit by applying it to a digital case study: a webbased, community health science database we are currently researching. We begin our article with a bit of background, including a review of esocial science and, more specifically, the SACS Toolkit. Next, we provide a brief description of our digital case study and the challenges it presented us; followed by an explanation of how we used the SACS Toolkit to solve our challenges. We end with a summary of how other systems researchers working with digital data may find the SACS Toolkit useful.
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01/01/2002
We wrote this essay to construct a grounded theory about how women in medicine experience the intersection of family and work. We were particularly interested in women who had children while in medical school or were practicing medicine parttime. These two populations interested us because they represented women who had made decisions about motherhood in direct opposition to the prevailing culture of medicine. Our results suggest that the dominant experience of these women is one of double consciousness: a sense of competing aims between family life and work, between an exchanger and obligated self, and between the embodiment of motherhood and physicianhood.
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12/01/2015
Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field's dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case‐based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7‐factor (20 biomarker) model of AL's network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro‐inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles. © 2015 Wiley Periodicals, Inc. Complexity 21: 291–306, 2016
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07/01/2016
This paper is part of a series addressing the empirical/statistical distribution of the diversity of complexity within and amongst complex systems. Here, we consider the problem of measuring the diversity of complexity in a system, given its ordered range of complexity types i and their probability of occurrence pi , with the understanding that larger values of i mean a higher degree of complexity. To address this problem, we introduce a new complexity measure called casebased entropy Cc — a modification of the Shannon–Wiener entropy measure H. The utility of this measure is that, unlike current complexity measures–which focus on the macroscopic complexity of a single system–Cc can be used to empirically identify and measure the distribution of the diversity of complexity within and across multiple natural and humanmade systems, as well as the diversity contribution of complexity of any part of a system, relative to the total range of ordered complexity types.
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03/01/2015
This article introduces a new case‐based density approach to modeling big data longitudinally, which uses ordinary differential equations and the linear advection partial differential equations (PDE) to treat macroscopic, dynamical change as a transport issue of aggregate cases across continuous time. The novelty of this approach comes from its unique data‐driven treatment of cases: which are K dimensional vectors; where the velocity vector for each case is computed according to its particular measurements on some set of empirically defined social, psychological, or biological variables. The three main strengths of this approach are its ability to: (1) translate the data driven, nonlinear trajectories of microscopic constituents (cases) into the linear movement of macroscopic trajectories, which take the form of densities; (2) detect the presence of multiple, complex steady state behaviors, including sinks, spiraling sources, saddles, periodic orbits, and attractor points; and (3) predict the motion of novel cases and time instances. To demonstrate the utility of this approach, we used it to model a recognized cohort dynamic: the longitudinal relationship between a country's per capita gross domestic product (GDP) and its longevity rates. Data for the model came from the widely used Gapminder dataset. Empirical results, including the strength of the model's fit and the novelty of its results (particularly on a topic of such extensive study) support the utility of our new approach. © 2014 Wiley Periodicals, Inc. Complexity 20: 45–57, 2015
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11/01/2012
Recently, the continuity equation (also known as the advection equation) has been used to study stability properties of dynamical systems, where a linear transfer operator approach was used to examine the stability of a nonlinear equation both in continuous and discrete time (Vaidya and Mehta, IEEE Trans Autom Control 2008, 53, 307–323; Rajaram et al., J Math Anal Appl 2010, 368, 144–156). Our study, which conducts a series of simulations on residential patterns, demonstrates that this usage of the continuity equation can advance Haken's synergetic approach to modeling certain types of complex, self‐organizing social systems macroscopically. The key to this advancement comes from employing a case‐based approach that (1) treats complex systems as a set of cases and (2) treats cases as dynamical vsystems which, at the microscopic level, can be conceptualized as k dimensional row vectors; and, at the macroscopic level, as vectors with magnitude and direction, which can be modeled as population densities. Our case‐based employment of the continuity equation has four benefits for agent‐based and case‐based modeling and, more broadly, the social scientific study of complex systems where transport or spatial mobility issues are of interest: it (1) links microscopic (agent‐based) and macroscopic (structural) modeling; (2) transforms the dynamics of highly nonlinear vector fields into the linear motion of densities; (3) allows predictions to be made about future states of a complex system; and (4) mathematically formalizes the structural dynamics of these types of complex social systems.
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