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 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  


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