Complexity Art

The above picture is another example of what I call complexity art. The technique is called assemblage (or, alternatively, assembled cubism). For more info, see my post from 25 April 2009.

The goal of assemblage is not to redefine the role of space or time in a picture. Instead, the goal is to pictorally represent complex systems--be these systems a single individual, two people in relationship, groups, humans and nature, humans and machines, etc.

Given this goal, the completed picture, while highly representational, is primarily symbolic. It is an iconic representations of a complex system--by icon I mean here a visual (semiotic) sign that stands in place of or acts as a simulacra of something else.

The above picture is an inconic representation of a mother and daughter. One can see the structural similarities between mother and daughter in terms of their eyes, neck muscles, etc. And yet, one loses a clear sense of who's part is which. Instead, the parts blend together to create a face that is neither the mother's or daughter's. This face is an emergent system entirely dependent upon the nuanced parts of which it is made. The result is a multi-singularity: multiplicity and difference within union and integration.


ASSEMBLAGE: Complexity Science Art

Sociology is not the only trajectory along which I have pursued the study of complexity. In fact, long before I figured out how to apply complexity science to sociology I was working on it in my art. As my geek t-shirt stuff suggests, art is part of my complexity agenda.

The above picture is an example of the type of complexity art I have been doing, which I call assemblage--partially in homage to the cubists and, more specifically, Robert Rauschenberg, the famous American painter.

In terms of technique, assemblage extends the work of Picasso and Braque by going beyond analytic and synthetic cubism into a new area, assembled cubism. Following Raushenberg, assembled cubism takes a complex systems approach to paintings, attempting to examine the inter-dependence and inter-connectedness of humans and the world in which they live. It also treats this inter-dependence and inter-connectedness as a system, where the whole is more than the sum of its parts. How, for example, can one paint two people, showing the entanglement of their relationship, to arrive at a whole; and yet, at the same time, allow the individuals to shine through?

The spirit of assembled cubism is found in the following quote from William Johnston: "When people meet at the level of personal love achieved through radical non-attachment, they do not merge, nor are they absorbed in one another.... There is at once a total unity and a total alterity" (Silent Music, 1976, p. 147, Perennial Library).

Complexity Art--Geek T-shirts

I have been working on a few new geek t-shirts shirts, which people have seemed to like. The whole idea behind these shirts is to promote complexity science specifically and science and math more generally, particularly amongst young people and kids.





Qualitative Comparative Analysis

For those interested in learning more about qualitative comparative analysis (QCA), here is the link to Ragin's overview, which provides lots of information.


As I stated in my post 7 April 2009 post, QCA provides the best option for integrating qualitative and quantitative method into a new toolkit for the study of complex social systems. Check it out.


The SAGE Handbook of Case-Based Methods

For the last several posts, I have been discussing the need for complexity science to truly overcome the qualitative/quantitative divide by doing more work to develop qualitative method. The next question, then, is how?

Of the various options available to complexity scientists, I think the best is case-based method. Actually, the better term is cross-case analysis. Cross-case analysis is an inductive approach to scientific inquiry that begins with a set of cases in order to explore what makes them similar to and yet different from one another. Cross-case analysis is very iterative and data-driven: the researcher develops ideas about the non-obvious patterns of relationship amongst a database by exploring its cases.

Perhaps the most well-known cross-case method is grounded theory, which was developed by Glaser and Strauss in the middle 1960s. While their method is referred to in the popular literature as grounded theory, they actually called it (at least initially) the constant comparative method, which they argued could be used to generate grounded theory. In other words, their famous book title, The Discovery of Grounded Theory was meant to imply that, through the constant comparative method one could generate grounded theory. Instead, the name Grounded Theory stuck.

In the sticking of this name, however, a major feat in the history of social science method was lost. In a paper I published in 2003, my colleagues and I made it clear that Glaser and Strauss never meant their method to be limited to narrative data. The constant comparative method could be equally applied to numerical or narrativel data. Grounded theory was not only a breakthrough in the popularization of cross-case analysis, it was a major breakthrough in the blurring of qualitative and quantitative method.

Here is a blurb from their book:

"Our position in this book is as follows: there is no fundamental clash between the purposes and capacities of qualitative and quantitative methods or data. What clash there is concerns the primacy of emphasis on verification or generation of theory—to which heated discussions on qualitative versus quantitative data have been linked historically. We believe that each form of data is useful for both verification and generation of theory, whatever the primacy of emphasis. Primacy depends only on the circumstances of research, on the interests and training of the researcher, and on the kinds of material he needs for his theory (1967:17–18)."

Grounded theory is not the only cross-case method. Others do exist. The problem, however, is these methods have not made it into the mainstream of sociological or social scientific inquiry.

What is fascinating to me is that, while case-based method remained on the margins of sociological inquiry throughout the 1980s and 1990s, over on the other side of the scientific fence, in the natural and computational sciences, cross-case method was being rediscovered. This time, however, it emerged in the form of distributed artificial intelligence, cluster analysis, data mining, decision-tree analysis, artificial neural networking, the self-organizing map algorithm, machine intelligence, genetic algorithms, fuzzy-set theory, fuzzy-set logic, and the host of robots and algorithms running our washing machines, cars, industrial machinery, traffic lights, the internet and, the soon to come, Web 2.0.

And still sociologists sit idle, believing case-based method is something wishy washy that qualitative type people do. Just like sociologists and many social scientists have sat idle and watched complexity science emerge.

We are out of the loop--big time! Trust me, I am not being dramatic. If you approached the average sociology professor or graduate student and asked them if they could implement any of the above methods I just listed from the natural and computational sciences, and could they do so while integrating these methods with qualitative methods to conduct qualitative, cross-case analysis of large, complex databases, they would probably say no.

Hence the need for David Byrne and Charles Ragin's forthcoming book, The SAGE Handbook of Case-Based Methods. Actually, the sub-title of the book should be qualitative, comparative analysis (QCA), because that is the method they have been advocating for several years.

It is great to see this book published. It is also great that it is a handbook, because that means other scholars are working with these ideas; and the fact that SAGE has published it means that QCA has, in some small way, gained the authority it deserves.

A quick review of the chapters in the book demonstrates the broad utility of cross-case analysis and, more specifically, QCA (click here to see the complete index). There are chapters integrating cluster analysis with case-based method, as well as chapters applying QCA to the analysis of large, complex, digital databases.

The book also goes a long way to integrating cross-case analysis with complexity science. Byrne and Ragin are major social science scholars in complexity science. In my book on Sociology and Complexity Science (SACS), for example, I identify them as two of the leading scholars in SACS--see my map of SACS. For example, Byrne wrote a very important book in 1998 titled, Complexity Theory and the Social Sciences. Ragin's related book is Fuzz-Set Social Science (2000).

For those interested in developing a method for studying complex social systems, Byrne and Ragin's book provides the necessary foundation. In the name of QCA, they bring together the best of qualitative and quantitative method in order to overcome both.


Grounded Neural Networking

The above publication is the type of work I am referring to as an example of developing qualitative method for studying complex systems. It is an article I wrote in 2003 integrating grounded theory method (a hallmark in qualitative methodology) with the artificial intelligence technique known as the Kohonen Self-Organizing Map. The result is a qualitative method for analyzing large, complex databases that draws upon the strength of traditional qualitative method and the latest advances in numerical analysis and, more specifically, data mining.

Santa Fe and qualitative numerical analysis

This post builds on yesterday's Qualitative/Narrative Complexity Science.

Part of my argument in the above post was that, in terms of qualitative method, the major advance complexity science makes is the qualitative study of numerical data. To demonstrate this point, click on the following link to the Santa Fe Institute (the leading world institute for the study of complexity) and, in the search box, type in "qualitative method." You will get roughly 700 hits. Almost all of them contain the terms qualitative and numerical.

You will find, however, almost no mention of qualitative method, as it is understood in the social science sense of the term. This is not to say there is no such work being done. But, it by no means has a dominant voice.

Qualitative/Narrative Complexity Science

For all of its advances (and they are many) complexity science has yet to bridge fully the rift between qualitative and quantitative method.

Before I explain myself, however, some quick definitions are in order. First, by qualitative method, I mean the non-numerical analysis of narrative and verbal data, as typically studied in historical inquiry, ethnography, qualitative interviews, and grounded theory. By quantitative method, I mean the study of numerical data, primarily through the application of statistics and top-down equation-based modeling.)

To its credit, complexity science has significantly progressed the qualitative analysis of numerical data. By "qualitative analysis" I mean the study of the complex, emergent, relational, dynamic, evolving, idiographic dimensions of numerical data. In fact, one could claim that complexity science method is really a major advance in the qualitative study of complex numerical data.

What complexity science has not advanced, however, is the non-numerical study complexity. To date, only a handful of articles have applied qualitative method to the study of complexity. And even fewer articles have examined how to advance the usage of qualitative method for studying complex systems.

The earliest examples I know of that apply qualitative method to the study of complexity were written by Crabtree and colleagues (most of whom are in medicine, nursing or health finance) and their study of medical practices:

1. Crabtree, B. F. (1997). Individual attitudes are no match for complex systems. Journal of Family Practice, 44(5), 447-448.

2. Crabtree, B. F. (2003). Primary care practices are full of surprises! Health Care Management Review, 28(3), 279-283.

3. Crabtree, B. F., Miller,W. L., Aita,V. A., Flocke, S. A.,&Stange, K. C. (1998). Primary care practice organization and preventive services delivery: Aqualitative analysis. Journal of Family Medicine, 46(5), 403-409.

4. Crabtree, B. F., Miller,W. L.,&Stange, K. C. (2001). Understanding practice from the ground up. Journal of Family Practice, 50(10), 881-887.

The earliest (and most widely popular) example of the development of qualitative method for the study of complex systems is Charles Ragin's Fuzzy Set Social Science (2000). Ragin also has a new book with David Byrne (a prominent British sociologist and leading scholar in the social science application of complexity science--I will blog more about this book later). The title of the book is The SAGE Handbook of Case-Based Methods (2009).

Despite being a small literature within complexity science, these scholars make some very compelling arguments for developing the qualitative (non-numerical) study of complexity. Perhaps the best argument is that a significant amount of data goes unexplored when qualitative method is not used.

What, for example, are the phenomenological dimensions of complex networks? What does it mean for people to be connected to one another by six or fewer links? What are the emotional dimensions of being part of a massive online social network? What role do power, conflict, hate, greed, anger, and love play in the complex global system? How does one study "confidence" in a system? What does a state of domination within a complex social system look like? Is altruism within a system more than a prisoner dilemna? I could go on and on and on.

Okay, just one more example: Think about the current global financial collapse in which most (if not all) the world is struggling? How do people make meaning of this experience? And, to consider second-order cybernetics and sociocybernetics, what consquence does the meaning people make have for the way in which our global economic system will evolve? And so on and so forth.

There is a lot qualitative method can offer complexity science. And, there is a lot complexity science can offer qualitative method. If complexity scientists turned their attention to this dimension of method, they could create some very incredible tools.