2/14/12

Case Based Method and Complexity Science, Part II (The SACS Toolkit)




In my previous post for February 2012--CLICK HERE TO SEE--I introduced the concepts of case-based complexity science and its methodological extension, case-based modeling--the twin concepts I use to describe the approach being developed by David Byrne, Charles Ragin and others for modeling and studying complex systems.

My goal here is to introduce the case-based complexity science method my colleagues and I have developed for modeling complex systems.  Our case-based modeling technique is called the SACS Toolkit--which stands for the Sociology and Complexity Science Toolkit.

For a more thorough overview of the SACS Toolkit, including the papers and book chapters we have written on it, CLICK HERE


THE SACS TOOLKIT

The SACS Toolkit is a case-based, mixed-method, system-clustering, data-compressing, theoretically-driven toolkit for modeling complex social systems. 

It is comprised of three main components: a theoretical blueprint for studying complex systems (social complexity theory); a set of case-based instructions for modeling complex systems from the ground up (assemblage); and a recommend list of case-friendly modeling techniques (case-based toolset). 

The SACS Toolkit is a variation on David Byrne's general premise regarding the link between cases and complex systems.   Byrne's view is as such: 
  
Cases are the methodological equivalent of complex systems; or, alternatively, complex systems are cases and therefore should be studied as such. 

The SACS Toolkit widens Byrne's view slightly.  For the SACS Toolkit:  

Complex systems are best thought of as a set of cases--with the smallest set being one case (as in Byrne's definition) and the largest set being, theoretically, speaking, any number of cases.


More specifically, for the SACS Toolkit, case-based modeling is the study of a complex system as a set of n-dimensional vectors (cases), which researchers compare and contrast, and then condense and cluster to create a low-dimensional model (map) of a complex system's structure and dynamics over time/space. 

Because the SACS Toolkit is, in part, a data-compression technique that preserves the most important aspects of a complex system's structure and dynamics over time, it works very well with databases comprised of a large number of complex, multi-dimensional, multi-level (and ultimately, longitudinal) factors. 

It is important to note, however, before proceeding, that the act of compression is different from reduction or simpli fication. Compression maintains complexity, creating low-dimensional maps that can be "dimensionally inflated" as needed; reduction or simplifi cation, in contrast, is a nomothetic technique, seeking the simplest explanation possible.

The SACS Toolkit is also versatile and consolidating. The strength, utility, and  flexibility of the SACS Toolkit comes from the manner in which it is, mathematically speaking, put together.  The SACS Toolkit emerges out of the assemblage of a set of existing theoretical, mathematical and methodological techniques and fi elds of inquiry--from qualitative to quantitative to computational methods. 

The "assembled" quality of the SACS Toolkit, however, is its strength. While it is grounded in a highly organized and well defi ned mathematical framework, with key theoretical concepts and their relations, it is simultaneously open-ended and therefore adaptable and amenable, allowing researchers to integrate into it many of their own computational, mathematical and statistical methods. Researchers can even develop and modify the SACS Toolkit for their own purposes. 

For a more thorough overview of the SACS Toolkit, including the papers and book chapters we have written on it, CLICK HERE  

 




4 comments:

  1. I cant help but notice the close affinity many of these concepts and methodological approaches share with the field of resilience ecology - conceptualising systems as assemblages of identity components (which fits well with a critical realist notion of ontological stratification), rejection of normative stability measures, retention of agency, methodological pluralism etc. Seems like an ideal route for productive dialogue

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    1. your comments are excellent and most interesting. i am not familiar with resilience ecology. any recommended readings?

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  2. Thanks, I've found it useful for making sense of agrarian systems - particularly those managed through common property arrangements, as most discussion tends to focus on rational actor models, or resource maximisation assumptions (i.e. classical homeostasis or methodological individualism).

    Resilience ecology emerged from a similar dissatisfaction with 'optimal state' assumptions in sociology - instead of assessing a systems' return time to base-state parameter values following disturbance, it examines (both qualitatively and quantitatively) the amount of disturbance a system may undergo before shifting state into a new regime. Its definition of system is not spatial, as it relies on defining the parameters of a systems 'identity' (i.e. common property arragements, high fertility, extent of commodity production).

    A new regime is entered when disturbance of sufficient magnitude alters the systems identity, which may occur through any number of factors or interactions of multiple ontological levels. I find it fits well within your own case-oriented approach, as it allows data-reduction techniques such as cluster analysis to discern the presence of attractors in the n-dimensional spaces such systems occupy (i.e. aggregate data at country/regional level may reveal certain clusters of areas occupied by the individual agrarian systems of interest as exhibiting certain macro-characteristics), and the concept of identity permits a form of ideal-typical modelling from multiple data sources at local level.

    I've found it retains much of the promise of complexity as informed by sociology by directing the researcher to examine adaptive capacity at local level (agency), whilst permitting certain judgements about qualitative differences of state, and structural similarity (i.e. avoiding the excesses of ANT inductivism).

    Apologies for the long comment (really enjoying reading through the articles and posts).

    These are a good representation of current thinking on resilience - many refer explicitly to the complexity paradigm (if such a thing may even be defined);

    Cumming, G.S.; Barnes, G; Perz, S; Schmink, M; Sieving, K.E.; Southworth, J; Binford, M; Holt, R.D.; Stickler, C and Van Holt, T. 2005. “An Exploratory Gramework for the Empirical Measurement of Resilience.” Ecosystems 8: 975-987

    Cumming, Graeme S. 2011. Spatial Resilience in Social-Ecological Systems. Springer Science

    Gunderson, Lance. 2000. “Ecological Resilience – in Theory and Application.” Annual Review of Ecological Systems 32: 425-39

    Gunderson, Lance. 2003. “Adaptive dancing: interactions between social resilience and ecological crises.” Pp. 33-52 in Navigating Social-Ecological Systems: Building Resiliennce for Complexity and Change edited by Fikret Berkes, Johan Colding and Carl Folke. Cambridge University Press.

    Holling, C.S. 1973. “Resiliance and Stability of Ecological Systems.” Annual Review of Ecology and Systematics 4: 1-23

    Taylor, Peter. 2011. “Conceptualizing the Heterogeneity, Embeddedness, and Ongoing Restructuring that Make Ecological Complexity ‘Unruly.” Pp. 87-96 in Ecology Revisited: Reflecting on Concepts, Advancing Science edited by Schwarz, Astrid and Jax, Kurt. London: Springer

    Walker, Brian; Gunderson, Lance; Kinzig, Annn; Folke, Carl; Carpenter, Steve and Schultz, Lisen. 2006. “A Handful of Heuristics and Soemm Propositions for Understanding Resilience in Social-Ecological Systems.” Ecology and Society 11(1): 13-28

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  3. Excellent! thanks for taking the time to explain that and for the readings. I have some reading to do.

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