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.


CHECK EM OUT.


SHIRTS RANGE FROM 11.99 AND UP. THERE ARE ORGANIC SHIRTS AS WELL, AMERICAN APPAREL, ETC.

TO BUT THESE SHIRTS AND OTHERS CLICK HERE

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.

CLICK HERE

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

(click here for article) GROUNDED NEURAL NETWORKING: MODELING COMPLEX QUANTITATIVE DATA.

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.

Rockin' Mandelbrot Song

CLICK HERE TO SEE THIS ABSOLUTELY ROCKIN' MANDELBROT SONG

This is absolutely the coolest math song ever written. I gave a MATH DAY presentation about two weeks ago for 300 math geeks and they went crazy! It is fanstastic. Play it for yourself, friends, profs, and students--especially students in the social sciences and humanities.

The song is by Jonathan Coulton. The video was made by Pisut Wisessing in Film 324: Cornell Summer Animation Workshop, taught by animator Lynn Tomlinson.

Dungeons & Dragons--the Geek Stereotype

Okay, so most of us geeks fit the stereotype--instead of going on dates in highschool with humans, we were dating elves (male or female) or any other assorted group of medieval characters. D&D anyone?

We geeks eventually grew out of this phase. Actually, no we didn't--which brings me to the point of this post. One of my geek buddies (Michael Ball) has gone and done the worst thing a medieval geek can do. He wrote a book about it.

Mike's first fiction book is titled The Stone Men. It is an excellent short story with fantastic illustrations drawn by Christopher Bort. Check it out. And, bewaare, the stone men are coming...

Map of Science

This is a great graphic overview of the increasing complexity and interdiscplinary nature of scientific inquiry. (As a side note, it also shows that the social sciences play a much larger role in science than typically acknowledged.) This graph was part of a recent article published in PLoS ONE on 11 March 2009.

Title: Clickstream Data Yields High-Resolution Maps of Science Johan Bollen1*, Herbert Van de Sompel1, Aric Hagberg2#, Luis Bettencourt2,3#, Ryan Chute1#, Marko A. Rodriguez2, Lyudmila Balakireva1

Great Blog: Social Media Today

When people take time to post a comment on this blog, I always take the time to read about their work. Recently, Tom Mandel posted a comment on "Is Foucault a Complexity Scientist?"

One of the blogs on his site is Social Media Today. This is a great site because it is part of the latest trends in internet life. But, it is also an observer of these trends. In short, it is part of the latest movement known as e-science.

As much as I enjoy the web, I find myself in that endless double-bind of participant and researcher. I am fascinated with the web, and yet my researcher side is always asking: What is going on here? Why am I participating in all this? What is this all about? But, no sooner do I ask such questions when I make another click and go: Wow, this is really cool and I've got to tell someone about this new technology or social network, or blog, etc, etc, etc, ugh!

It is because of my double-bind that I really like the blog, Social Media Today. It is a participant in and researcher of the latest trends in information and the forthcoming Web 2.0. Very good stuff for those complexity scientists and sociologists interested in life on the web and where things are going.

Complexity 1001: One More Question

Another overwhelming aspect to sticking my toe into the complexity rapids is the number of new concepts and terms I have encountered (from agent-based modeling to neural networking to fractal geometry, etc.).  So -- in addition to a key/core reference(s) -- what would be the half dozen or so key concepts or terms I would need to master so I can build a foundation in understanding complexity science?  I'm not sure why, but I imagine myself standing on a beach with dozens upon dozens of interesting looking shells -- and while I can picture myself picking up any one of them here and another one or two of them there -- and eventually working my way across all of the shells -- I suspect there would be some shells that are "basic" and thus fundamental to understanding all shells -- and I would appreciate your suggestions here as well.


Dr. Castellani's Reponse


Dear Complexity Challenged, I would start with my complexity science map. Here is why.

The map is conceptual.

Like you, I struggled early on to get a grasp of this field. It is so amazingly interdisciplinary and scattered that it is hard for the beginner (and even expert) to have a true appreciation for what is going on with the field as a whole. After years of struggling to obtain some type of synthesis, I realized that some degree of closure could be obtained if I looked for similarities across the wealth of research taking place. I asked myself, what concepts (be they theoretical or methodological) do all complexity scientists use? And, how do these concepts relate? Also, could I identify the leading scholars associated with these concepts? And, could I highlight one particular sub-concept or area of study with which each of these scholars could be identified? The result was the map.

So, long story short, I would work on mastering the concepts on the map. That will give you an excellent working knowledge and vocabulary sufficient to communicate with any complexity scientist, regardless of their otherwise intractable or incomprehensible research--hee haw!

Complexity 1001: Getting Started

Professor Castellani:  I want to begin a study of complexity -- as it applies to sociology and to issues of healthcare, but I am not sure where to begin.  I've done a bit of googling, read through some of the materials on your site (loved your Complexity Science Map BTW), visited amazon.com -- and at the end of it all, feel a little overwhelmed.

I saw the link for Complexity 1001 and thought I might use it to jump start my learning.

Where would be a good place to start?  What article (book chapter etc.) could you suggest -- something to get my feet wet.  Perhaps from here I could raise a question or two for subsequent discussion, pick up another yet another suggesting resource or two, and go from there?

Thanks

DR. C'S RESPONSE

Dear Complexity Challenged, thanks for becoming part of this blog. I think the best way to "jump in and get your feet wet" is to take a historical macro-level approach and begin with two of the best known reviews of the field.

1. The first is Capra's The Web of Life. While written in 1997, this book still provides the best introductory review of complexity science and its historical roots--in particular, systems science, cybernetics and artificial intelligence and their links to the major themes in complexity science.

2. The second book is Waldrop's Complexity. This is another excellent book because it covers what Capra misses--the historical development of the Santa Fe Institute, the first and most important institute involved in the creation of complexity science and its most cutting-edge research. Almost every major figure in complexity science during the 1980s and 1990s had something to do with Santa Fe. Complexity is a bit journalistic and sensationalist (even gossipy) in style, but it really does give a good historical account of the early years of complexity science.

Most important about The Web of life and Complexity, they introduce you to all the major concepts of complexity science: emergence, self-organization, tipping-points, autopoiesis, self-organizing criticality, computational economics, cellular automata, agent-based modeling, fractals, chaos theory, networks, and so on.

These two books also introduce you to the major players during the 1980s and 1990s: from Holland and Kauffman to Prigogine and Bak to Matarana and Varela.

Once you have a basic sense of the field, you can move to a review of the methods of complexity science. Here is where things become more technical and less macro. You start to move down to the meso and even micro level, exploring specific topics like neural networks, agent-based modeling, the new science of networks, fractals, modeling complex systems, power laws, etc.

But, let's not get into the deep section of the pool too quick. I would get those two books and read them first.

Is Michel Foucault a Complexity Scientist?

In 1999 I wrote an article for Studies in Symbolic Interaction titled, Michel Foucault and Symbolic Interactionism: The Making of a New Theory of Interaction. The article sits at the heart of the theoretical framework (social complexity theory) that Hafferty and I outline in our new book, Sociology and Complexity Science: A New Field of Inquiry. Our theoretical framework, in turn, is part of the SACS Toolkit, which is our new method for modeling complex social systems.

While it may seem odd to some, my journey into complexity science is through the work of Michel Foucault, particularly his later theory of social practice. For me, Foucault’s work has always been about complex social systems and their impact on individuals.

From Madness and Civilization to The Archeology of Knowledge to Discipline and Punish, what are Foucault’s books about? Think about it. At least theoretically and methodologically speaking, they are about complex social systems! Foucault is trying to understand, in post-structural terms, how systems go from one state to another—from one set of self-organizing relations to another. How, for example, does the care of mental disorders, prisoners, deviants, or the self in the west go from a medieval apparatus of care to a modern apparatus of care?

Given this orientation, could we not call Foucault’s work the study of tipping points? Is not Foucault studying how complex social systems evolve over time to become something new, where they suddenly shift from one self-organizing form to another as a function of some type of punctuated equilibrium, some type of major phase shift? Is that not what Foucault’s whole discourse is about, along with the impact these shifting systems have on individuals and their care of self?

Also, could we not call his early work (up to Archeology of Knowledge) a top-down approach to system modeling? Something similar to Luhmann’s view of systems? I mean, is not Foucault, at least early on, trying to understand how systems change without having to call upon some micro-level theory of agency? Something Luhmann and Parsons and others tried to do? Is Foucault not also trying to understand the system within the confines of the system itself?

Then, beginning with Discipline and Punish and his interviews in Power and Knowledge, is not Foucault suddenly grounding his complex systems view in social practice? Suddenly shifting to a bottom-up perspective? Is that not what his methodological shift from archaeology to genealogy is all about? Top-down to bottom-up? A macro to a micro level shift in orientation?

Think about it? How would Foucault sound if he talked about dispositifs and apparatus as complex systems? What if he talked about apparatus which obey their own internal logic as emergent self-organizing systems? What if Foucault talked about his post-structuralism as a way of talking about history as changing dynamic systems that do more than just follow the dialectic? What if he talked about complex social systems that evolve over time along multiple trajectories? Suddenly his idea of systems containing their own resistance (his Nietzschian theory of power) makes more sense: we are talking about the multiplicity of systems, differentiation and feedback loops. And, suddenly his ideas would not seem so unique—at least by today’s knowledge of complexity science. Suddenly his ideas sound less structural and more systems-oriented.

Because this is a blog, I will not blag on too much. So, just consider one of Foucault’s key concepts, the dispositif. For Foucault, this concept forms the field of relations in which his work, up to the end, is situated within.

Foucault states: "What I’m trying to pick out with this term is, firstly, a thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural forms, regulatory decisions, laws, administrative measures, scientific statements, philosophical propositions, moral and philanthropic propositions--in short, the said as much as the unsaid. Such are the
elements of the apparatus [dispositif]. The apparatus [the grid of intelligibility] itself is the system of relations that can be established between these elements. Secondly, what I am trying to identify in this apparatus is precisely the nature of the connections that can exist between these heterogeneous elements (Language, Counter-Memory, Practice, 1980, p. 194)."

As this quote shows, Foucault's work is always about mapping the grid of intelligibility (the dispositif) for some complex system in historical time-—be the system medicine, mental health, the social sciences, criminal justice, psychoanalysis, religion, or government. For Foucault, the dispositif is a system’s self-organizing order of things, its field of organizing practices. But this dispositif is not a totalizing system of relations as in the dialectic. Nor is it something the historian simply uncovers. It is both the interpretive framework that the historian imposes upon the discourses of the past (which is why Foucault often refers to his works as fictions, 1991, p. 33) and the relations that exist between the various discursive and nondiscursive heterogeneous elements making up the field of organizing practices—I mean, does that not sound like 2nd order cybernetics or sociocybernetics? The dispositif is a system of strategies that exist as practice, both on the part of the historian and on the part of the period in question. The dispositif isn’t found within some external structure or within the heads of particular controlling agents. It is within the practice of practice itself. It is fragmented, disjointed and broken, and yet inter-related, unified and organized. It is not a Parsionian system that exists as homeostasis, which then requires us to explain how change happens. It is a changing system where we question how order itself is possible.

Again, this is just a thought. But, it does open up the possibilities for some incredible connections between the last twenty years of sociological inquiry and the new science of complexity. To see a more thorough argument of my point of how Foucault can be used to build a theory of social complexity, see our new book, Sociology and Complexity Science.

I've got a bunch of new geek t-shirts at my CAFE PRESS STORE. Check em out. I particularly like this one and the irony of it.

Factory Wiz

Complexity 1001

Starting today, I will be featuring a new segment on this blog, called Complexity 1001. Like the name sounds, Complexity 1001 will provide an undergraduate (college) level introduction to complexity science and, related, the intersection of complexity science with the social sciences, specifically sociology.

I have asked a few friends who are new to complexity science (a couple profs and a couple students) to post any questions, concerns, or issues they have as they learn about and apply the tools of complexity science.

I also welcome anyone else to post questions they would like answered. You can email me at factory.as@gmail.com or you can post a question in any of the recent Complexity 1001 postings.

Any time I respond to a post, the heading of my post will always be Complexity 1001. This way you can find older postings as the months go by.

Finally, make sure you sign up for a posting feed or all comments feed so you get Complexity 1001 sent directly to your email or whatever place you daily go to see what's happening on the web!


So, let the online course and the postings begin.

Reprise: Intersecting the Study of Social and Complex Networks

Several blogs ago I posted on the need for researchers to do more work intersecting the new science of networks (complexity science) with the sociological literature on social networks, in particular the global network literature. Some sociologists do not see much to be gained from such a merger. For those resistant to the idea or unclear as to what such a merger is about, you need to read Vega-Redondo's Complex Social Networks.

The purpose of this book is to outline, in detail, the avenues of study that emerge from the intersection of the new science of networks and social network analysis.

Rather than going on, I recommend you go to Josep Pujol's excellent review of the book, published at JASSS (Journal of Artificial Societies and Social Simulation)

One note is, however, necessary. Given that my blog caters to social science students and researchers new to complexity science, it is worth mentioning that Vega-Redondo's book primarily makes its case through mathematics. Do not let that scare you away. It is something social scientists have to get used to: complexity science makes extensive use of mathematics to make its arguments. Social scientists are often poorly trained to deal with equation-based modeling. They receive little training outside the study of statistics. We need to get past this hurdle to adopt a much broader and stronger toolset. Having said that, here is one such opportunity to learn something new. Your hard work moving through such a book is worth the effort!

Complexity 5

Okay, so I just posted on Gershenson's blog, COMPLEXES and now I am posting on his recent book, Complexity 5.

I have to admit that this is the exact book I have wanted to write myself. It is a series of overviews (interview style) of leading thinkers in the field of complexity science.

What I particularly like about the book is that it interviews people who other complexity scientists view as TOP NOTCH--rather than the same list of popular people who often get far too much attention. I am particularly excited to see Nigel Gilbert, Paul Cilliers, and Bar-Yam in the list, as well as Melanie Mitchell. There are lots of women in complexity science who have yet to get their dues, and so this is great! (I cannot help making the last point, I am a sociologist.)

Here is the complete list of contributors: Peter M. Allen, Philip W. Anderson, W. Brian Arthur, Yaneer Bar-Yam, Eric Bonabeau, Paul Cilliers, Jim Crutchfield, Bruce Edmonds, Nigel Gilbert, Hermann Haken, Francis Heylighen, Bernardo A. Huberman, Stuart A. Kauffman, Seth Lloyd, Gottfried Mayer-Kress, Melanie Mitchell, Edgar Morin, Mark Newman, Grégoire Nicolis, Jordan B. Pollack, Peter Schuster, Ricard V. Solé, Tamás Vicsek, Stephen Wolfram.

Great Blog: Complexes

I have been following another excellent blog: COMPLEXES.

Complexes is run by Carlos Gershenson, who is a bit of an iconoclast. He has very broad interests in complexity, computer engineering, artificial life, and complexity-based art. He is also the book review editor for Artificial Life and the editor-in-chief of Complexity Digest--the leading compendium of all things complexity on the web.

(As a side note, if you do not have Complexity Digest bookmarked, please do so now. Also, Gottfried Mayer, the founding editor of Complexity Digest and leading systems/complexity scientist, passed away last month.)

Complexes is a fantastic blog because of the range of topics it addresses; and because Gershenson writes in a fresh way, with an insider's insights into various concepts, tools and techniques.

Also, check out his art.

I highly recommend the blog!

Widening the study of global networks

It is time to widen the complexity science vocabulary on global networks. Two rather disparate literature currently exist:

First, there is the new science of networks, and its very specific focus, web science. This literature is dominated by the work of Watts, Newman, Barabasi and scholars in the natural sciences. Web science is a specific focus, examining the world wide web and internet.

Second, there is the globalization literature, and its very specific focus on network society. This literature is dominated by the work of Wallerstein (world systems theory), Manuel Castells (global network society) and John Urry (mobile society and global complexity).

While these two literature are outstanding, not much has been done to bridge them. The closest example from the globalization side is Urry's work in Global Complexity. Related is Wellman's work on web science.

Again, these two literature differ in scholarly background--the first comes from physics and the natural sciences, while the second comes from sociology and political science.

They have lots to say to one another. The global network society literature has a lot to say on the social factors within which global networks are currently situated. The new science of networks has a lot to say about how the structure and dynamics of global networks work--for example, see Barabasi's recent article in nature on mobility.

Disserations, masters theses and funded research await those willing to integrate these two viewpoints in empirically grounded ways.

What are we? Complexity science, complexity theory, complex systems, complex self-organizing systems, etc????

Complexity science has been around long enough for the field to finally settle on a name. Systems science has a clear name, as does cybernetics and agent-based modeling. Complexity science needs similar clarity.

To demonstrate the current lack of clarity, do a basic search on Wikipedia, or examine the various web trackers. It becomes quickly clear that fuzziness and chaos abound--and I do not use these terms in a positive way.

Not having a name is a problem for a new science. It makes it hard for people to know what they are doing.


I strongly recommend complexity science as a name. Here is why

Not Complexity theory: Complexity is more than a theory. In fact, i would like someone to show what complexity theory is? I have yet to see a theory of complexity. I have seen complexity theories about evolution (Kauffman); social systems (Luhmann); organizations (Cilliers). But, I have not seen a complexity theory. No such thing exists.

Not complex self-organizing systems: Of all the possible terms, this one comes close, but it is too cumbersome. Complexity science is definitely the study of complex, self-organizing systems. However, complexity science is broader than just self-organizing systems. It deals with a variety of complex systems. Also, complexity science is cleaner and terse.

Not Chaos theory: While complexity science is indebted to chaos theory, it is something else. It is interested in organized chaos.

Not Agent-based modeling: While complexity science makes use of agent-based modeling, complexity science is more than just method.

Not e-science or web-science: This isn't going to work because the former is too substantively focused and the second is all method and often not systems oriented.

Not Post-systems science or Post-cybernetics: Complexity science is indebted to systems science and cybernetics--these fields are the historical lineage upon which complexity science is based. But, complexity science makes a break with these two fields, turning to a much larger literature to define its theories, methods and substantive problems.

Not Complexity: Complexity science is not just complexity. This term is too wide and ambiguous--we have always had complexity.

Not Computational Complexity: Computational complexity is too focused: it has to do with computational problem solving, not the study of complex systems.


And still more:The other reason complexity science is preferrable is because it separates the field from metaphorical, political and spiritual uses of this new science. A major criticism of complexity science today, particularly in the management literature, is a lack of rigor. Can a car company really be autopoietic? I doubt it. Is emergence some kind of quasi-spiritual mysterious force? If it is, then science might as well stop studying crowd behavior and the standing ovation problem.

Complexity science is a science. Let's call it that.

What Does 6-Degrees of Separation Mean? Or, why sociology is important to complexity science

In the 2002 January/February issue of Society, Judith Kleinfeld published an interesting article titled "THE SMALL WORLD PROBLEM." Kleinfeld's article is an excellent review and critique of the small-world problem--the idea that, in very large social networks everyone is connected to everyone else in the world by 6 or fewer links. The reason: networks are not random; instead, they contain weak-ties sufficient to link up everyone.

More important, Kleinfeld's article demonstrates the importance of sociology to complexity science. While physics can be used to study society, it needs sociology. Social systems are not physical systems--for the record, Watts agrees with this point (See Watts 2004).

For example, while a poor, female living in Mexico may be separated by less than six-degrees from a rich male living in Germany, it is very unlikely this poor female can make use of her links the same way someone of a higher socioeconomic status could. Sociology (and social network analysis, specifically, along with the study of kinship networks and health) has a lot to say about the quality of the connections in large social networks--above and beyond such terms as weak and strong ties, triangles, centroids, clusters, etc.

A whole language (all of it sociological, and much of it within social network analysis) awaits to be intersected with the new science of networks. This language includes community health, inequality, social stratification, medical sociology, gender, occupations, etc.

To learn more about the sociological approach to the new science of networks, see Barry Wellman's website and the INTERNATIONAL NETWORK FOR SOCIAL NETWORK ANALYSIS, See also Kleinfeld's COULD IT BE A BIG WORLD?

What Happened to Neural Networking?

NOTE: This post is one of my rants. It is not based on serious data analysis. Instead, it is an impression I have had for a while. If you think I am wrong, let me know. If you think I am right, let me know--that would make my day! ;)
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Back in the 1990s artificial neural networking was everywhere. The number of conferences, journal articles and grants devoted to its exploration was phenomenal. Then, suddenly, it seems, everyone moved on. Now, the rage is social networks.

I am not saying neural networks has been completely dropped. There is still some exciting stuff going on. But, it just never got integrated into the mainstay of complexity science method the way one would think.

And yet, neural networking is a major line of thinking in complexity science. As shown in my map of complexity, cybernetics and artificial intelligence (specifically distributed artificial intelligence)

One particular area that has yet to be fully appreciated by complexity scientists is Kohonen's self-organizing map--known as the SOM.

The SOM represents the latest advance in what can be called "qualitative computing."

By this term I mean that, the SOM is ready-made for finding nonobvious patterns in very large, complex digital, numerical databases. However, unlike statistics, the SOM is not driven by traditional hypotheses; it is not governed by the linear model; it searches for patterns of difference rather than aggregate norms and trends; it focuses on the relationships between conceptual indicators rather than the most powerful single variables; and, most important, while “intelligent,” it is actually dumb: the SOM does not tell you why it arrived at the results it gives you. There are no t-tests of significance to tell you what you found.

Instead, the SOM's output is open-ended, visual, and intuitive. To make sense of the nonobvious patterns and trends found, the researcher must apply traditional qualitative techniques--including coding, memo writing, and theoretical sampling. The qualitative orientation of the SOM does not mean one does not use statistics or formal mathematical modeling. I use these techniques all the time with it.

But, it does mean that the SOM is both computational and qualitative--a rare thing in method.

The SOM can do all of this because it is essentially a data reduction technique--while preserving the complexity of a system, it reduces its complexity to a 2-dimensional grid, onto which it projects the nuanced relationships between a set of factors. One combs this grid and the underlying factor structure to determine the dominant ways a data set clusters and the set of factors responsible for this clustering.

Familiar examples of the SOM are facial pattern recognition, analysis of disease trends, tumor detection, and primitive learning in robots and smart machines (See Kohonen 2001).

So, why aren't complexity scientists, particularly those in the social sciences, using the SOM? I do not know. Perhaps there is just so much going on that we have not reached an integration point. A method is explored, applied, developed and then everyone moves on to the next big method. Complexity science has not reached the point where multiple methods are combined to create a toolkit.

The other reason I think the SOM is not widely used, particularly amongst social scientists, is because of the geek factor involved. For example, I run Kohonen's free-ware program--the SOM Toolkit--in Matlab. If you cannot program your own neural net or you are not comfortable with Matlab or other programs with a high geek factor, it can be a bit overwhelming making use of this method. That, more than anything, is probably the unspoken reason neural nets and the SOM have not made a major splash in the social sciences. They are not overly easy to use.

They also do not fit the traditional paradigm of being numerical and quantative. Social scientists have an emotional breakdown when a method cannot be classified as qualitative or quantitative. Worse, if a numerical method does not have a t-test or some exact statistical way of determining the significance of its results, they just lose it! :)

Anyway, it just seems the SOM can be used to advance complexity science. For example, it can be used to explore how people cluster in a social network; it can be used to create conceptual maps of a complex systems; it can be used with agent-based modeling to improve the intelligence of agents, etc.

Again, I am not saying that the above types of work are not being done. I'm just saying that it seems more could be done.

What do you think?

The Complexity Blog: By Graduate Students for Graduate Students

I have been following an excellent complexity science blog for the past couple of weeks. It is called COMPLEXITY BLOG and it is run by two graduate students at the University of Michigan, who are affiliated with the university's international and renown Center for the Study of Complex Systems.

The two students are Kenneth Zick and Aaron Bramson--although the majority of recent posts are all by Bramson.

What is excellent about the site (even for undergraduates) is its cross-disciplinary viewpoint and breadth! For example, in addition to the blog, they provide a host of additional resources, which includes just about everything a student needs--from maps of the schools with complexity science programs to an excellent glossary to a good overview of all the major centers in complexity. I looked over their stuff and it is authoritative. You will not be led in the wrong direction.

I strongly recommend including this blog in your favorites! I put it in mine.

CALRESCO: A Good Educational Website on Complexity

Several years ago I came across an excellent educational website on complexity--at a time when few such websites existed. In the late 1990s you basically had the Santa Fe Institute Website and a few other things.

Then came along CALRESCO, which stands for the Complexity & Artificial Life Research Concept for Self-Organizing Systems. If you typed on Google or any other web browser words like "complexity" or "complex systems" the top hit was from CALRESCO.

Now, Wikipedia dominates, which is a good thing. Survivial of the fittest! However, the drawback is that an excellent educational website is getting less attention.

What makes CALRESCO so good is the level of detail. When I was first learning the numerous concepts of complexity science, CALRESCO not only provided excellent information, but it had lots of papers I could download to read, and they were at a basic level without being basic--does that make sense?

Anyway, if you want to get a good grasp on the basic cocnepts of complexity science, I recommend making this part of your favorites list.

Excellence: Springer Complexity Series

If you are looking for excellent books and journals on complexity, you have to check out the Springer Complexity Series

I took the following paragraph directly from their website:

About this series
Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition (typically many different kinds of components interacting with each other and their environments on multiple levels) and in the rich diversity of behavior of which they are capable. The Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of self-organizing dynamical systems at all levels of description and in all scientific fields, especially newly emerging areas within the Life, Social, Behavioral, Economic, Neuro- and the Cognitive Sciences (and derivatives thereof): Second, to encourage novel applications of these ideas in various fields of Engineering and Computation such as Robotics, Nanotechnology and Informatics: Third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs and selected edited contributions from specialized conferences and workshops aimed at communicating new findings to a large transdisciplinary audience.

Springer Book on Sociology and Complexity Science

My new book, Sociology and Complexity Science: A New Field of Inquiry was released by Springer on 4 Feb 2009. The book is part of Springer's complexity series.

To go to the book at Springer, CLICK HERE

Wikipedia Article on Sociology and Complexity Science

If you want a quick overview of sociology and complexity science and its major areas of study, see the Wikipedia article I recently posted on Wikipedia.

Complexity Map posted on Wikipedia

If you type in COMPLEXITY at Wikipedia, you will find my new map of complexity science there along with the article.

CLICK HERE TO GO DIRECTLY TO THE WIKIPEDIA ARTICLE ON COMPLEXITY
The map seems to be of use to people. The article is getting about 500 hits a day and the electronic map is getting about 50 hits a day. I hope it is of use to people. If you have questions about the map, post them here.

New map of complexity science

I just put together a new internet-based map of the field of complexity science, including all its major areas of study, leading scholars and intellectual lineage. It is an excellent resource for learning about complexity. In fact, I will make use of this map throughout my postings. CLICK HERE TO VISIT THE MAP

Complexity Science Geek T-Shirts

Hello fellow complexity science geeks.

You have got to check out the complexity science t-shirts I put on Cafe Press. In the vernacular of the 1970s, "they totally rock!" Take a look. Also, if you have a t-shirt idea you would like me to make, post a comment. Or, just post a comment on what you think of the t-shirts.

Programs in Complexity Science, Network Analysis or Agent-Based Modeling

I am trying to put together a comprehensive list of the degree programs students can complete to earn a degree is complexity science, agent-based modeling or network analysis. Please, please, please post any program--undergrad, masters, doctorate or certificate--of which you know.

Brian

In the fall (2008) we attended the ICAM seminar at the Center for the Study of Complex Systems, at Univ of Michigan. We heard a great presentation and got to talk with John Holland. very cool! He is absolutely brilliant. And, as of this year, an octogenarian.At the seminar we presented our new paper "Addressing the U.S. Financial/Housing Crisis: Pareto, Schelling and Social Mobility." click on the paper to run or download the agent-based model we used in it.

For those into complexity science, one of my personal favorite sites is BarabasiLab. It is run by the world-renown network scholar, Albert-Lazlo Barabasi. You have to check out the amazing network images he has posted!

I have been looking at the grants at the National Science Foundation (specifically, in the social and economic sciences section) and was amazed at the number of funding opportunities for work in computational thinking, complex systems thinking and building virtual environments. has anyone seen other funding organizations interested in these types of grants? i was really impressed. complexity science seems to have made major inroads into science

A question I have been thinking a lot about lately, given the upcoming sociocybernetics conference in Urbino Italy, is: what is the link between e-science, web science and complexity science? There seems to be tremendous overlap. Scholars like Barry Wellman and Barabasi do web science, but are complexity scholars. Manuel Castells is a sociologists who studies global network society, but a major part of his work is web science. And, all of the tools of complexity science are e-science, computational thinking tools. this seems to be a worthy paper or at least blog discussion. Any ideas?

Welcome to the Sociology and Complexity Science blog, or SACS blog for short. Here we will be posting all things related to the intersection of sociology and complexity science, as well as the related areas of e-science and web science. A specific focus of the blog is the application of SACS to the study of health and health care.