Maurizio Galimberti and Dynamical Complexity Photography

I recently came across the work of artist and photographer, Maurizio Galimberti.  I was instantly blown away. 

To me, Galimberti's work has taken David Hockney's photo-assemblages in a very new and interesting direction, showing how the camera, used in an immediate manner (Galimberti's prefers the polaroid), can capture the evolution of time and space and the dynamics of movement, without falling into the linear form of video or the graphic novel.  It also advances Italian Futurist photography--and this is made explicit in reviews--specifically the work of the Bragaglia Brothers, whose work you also have to see.  And, of course, one cannot forget cubism.

It is wonderful, wonderful stuff and another excellent example of visual complexity.

I couldn't find a picture by Galimberti that was free for public use, so I chose this Polaroid picture instead; which I made of my sister-in-law, Debbie, back in 1995, when I was just starting to really develop my own approach to photo-assemblage. 

Here are some great links to Galimberti's work:

Here is a link to this own website.
Here is a link to some of his recent photographs of current celebrities, such as Johnny Depp, Lady Gaga and Benicio del Toro.
Here is another link to read a bit more about him.
Here is a great video of him photographing Chuck Close.
Here is his facebook page.

 

Keynes Was Right: Government Austerity in Times of Trouble is a Bad Idea

I am sure many of you have seen the recent study by Thomas Herndon, a 28-year-old economics grad student at UMass Amherst.  If you have not, it is titled "Does High Public Debt Consistently Stifle Economic Growth? A Critique of Reinhart and Rogoff." (At the cite you can read the article, download the data, see responses to it and so forth.)

For a very good quick overview click here.

Here is an even shorter summary:

The article is a devastating critique of the recent austerity approach advocated in the states and other countries, which cynically seeks to cut and slash federal and state government into fiscal submission, while big business continues its massive growth into highly dynamic global markets, making more money than ever before.

What makes it so devastating is that Henderson uses Reinhart and Rogoff's own data: it appears that the initial authors made a major, major, glaring database error, forgetting to properly code information correctly; the initial authors also left out three of the countries (Canada, New Zealand, and Australia) where a charitable and relaxed approach to spending resulted in significant success.

What also makes the paper interesting from a complexity sciences perspective is the issue of curving fitting and Herndon's approach to nonlinearity versus the approach taken by the initial authors.

(The Figure to the right was taken from Herndon's paper.)

Anyway, I think it is a very good paper to read; and, for those of us who teach statistics and method, it is a wicked reminder to share with our students: always, always, always, check your data; and then check your data again!!!!!!!  Out of all the things my mentor, Galen Buckwalter taught me, that is one of those things burned onto the back of my brain.







   

Complex Systems Are Not Networks; They are Cases.

Let me be clear right from the 'get go' of this post!  I am a huge fan of network science!

I regularly teach the stuff in my college courses; I do network research myself; and my colleagues and I have incorporated adjacency matrices and relational data into our development of our novel approach to case-based modeling.  In fact, in my humble opinion, network science is probably one of the most important intellectual triumphs of science in the last twenty years!  

So, there!  I think my opening point is well made: in the vernacular of the 1970s, network science rocks!

Still, it is not everything!  What?

Let me explain.  See, it really hit me the other day.  I was thinking about the case, right?

More specifically, I was thinking about David Byrne's case-based-complexity-science notion that cases are complex dynamical systems c_i (j), where j denotes the time instant t_j.

I was also thinking about case-based modeling, the version of case-based method that my colleagues and I have developed, which (pace Byrne) treats complex systems as a set of cases, each its own complex dynamical system.

So, what is key to both views?  It is this idea that cases are these complex things,  In fact, in our work, they are so complex that we treat cases as k dimensional row vectors (c_i = [x_i1,...,x_ik]) comprised of a set of measurements—which, usually, given our health science focus, constitutes some combination of clinical, compositional or contextual variables.

So, what does all this have to do with networks?

Well, actually, a lot.  See, an obvious point just sort of suddenly hit me.  But, as with many things in life, sometimes the obvious can go unnoticed.

Put simply: 

1. Cases are more than nodes in some adjacency matrix.  Said another way, there is more to a case than its position within a network or the relationships it shares with other nodes. Cases are complex, comprised of characteristics (measurements) that are beyond (cannot be reduced to) the relational.  

2. In turn, therefore, complex systems cannot be reduced to (or studied solely as) networks, as the agents of which these systems are comprised are not just nodes or positions within some network.  In other words, because network science only studies cases as nodes, it does not constitute the robust model of complex systems it is generally touted to be.  Network science maps only one particular dimension (the relational) of the complex systems it studies.

Now, don't get me wrong.  I know that just about anyone is network science would respond to my insight with the retort, "No duh!"  So, I am not trying to construct a straw-person here.

What then, exactly, am I constructing?

I am constructing a caveat to network science and, more broadly, the complex sciences, that I think worth a few moments thought.

I read an interview, recently, with the noted physicist, eco-systems theorist and complexity scientist, Fritjof Capra--click here to read interview.  

Capra is the author of one of my all-time favorite books, The Web of Life, and is a major advocate of networks as one of the key patterns of life.  In fact, the point of the interview was to discuss Capra's views on the utility of networks for understanding complexity.  In fact, the title of the article is Networks as a Unifying Pattern of Life Involving Different Processes at Different Levels. 

However, in his opening argument, Capra makes the following point.  He basically argues that, while studying networks as patterns of organization is necessary, is in insufficient; the nodes, as cases, need to be understood.  This is particularly true of social networks, where the nodes help us understand deeper aspects of a complex system, including such things as meaning, symbolic interaction, culture, politics, the complexity of the people the nodes represent, and so forth.  He states:

Although I can observe a network pattern, I cannot really understand it if I don't know what an enzyme is, and how it interconnects various processes as a catalyst. Similarly, in a human community the network pattern is a pattern of communications. It interconnects individual processes of communication that create ideas, information and meaning. So, we need to address the question of meaning in terms of social science, political science, anthropology, philosophy, history and so on. The social sciences and the humanities have to be drawn in to deal with the level of meaning. Only then will we really understand what's going on in a community. We can draw diagrams, and people do that. They say, person A has 4 connections in a company and person B has 6 connections; they draw little stick figures and show how they are connected to other stick figures. But to me, it does not mean much because they don't deal with the dimensions of meaning, of culture, of consciousness. So, to come back to the original issue, a unified theory is unified only through the patterns of organization [networks], but it's not a complete theory. I don't even call it a theory, I call it a unified view of life, mind and society. And it's the pattern of organization, the formal aspect, that interconnects the different domains, but the content and the nature of the processes are different in each domain. 

So, following Capra, my caveat is simple: 

remember that nodes are not just nodes; they are cases!  As such, complex systems are not networks; complex systems are sets of cases.

  

  

 

Complexity Science and the UK's new Quantitative Methods (QM) Programme

The UK is currently implementing a major, new academic initiative to address, at the undergraduate level, the underdeveloped methodological skills of students majoring in the social sciences, particularly in the areas of quantitative method and statistics.

The initiative is called, appropriately enough, the Quantitative Methods (QM) Programme.  As it states on their website, the UK's QM is supported by "the Nuffield Foundation in partnership with the Economic and Social Research Council (ESRC) and the Higher Education Funding Council for England (HEFCE). The £15.5 million programme will promote institutional change; produce a first cohort of quantitatively skilled undergraduates; and create links between undergraduate and postgraduate training. Its ultimate purpose is to benefit academic research and meet the needs of the wider labour market."

The QM is a very impressive goal, something the states and other countries would likewise do well to think about implementing.  When one considers, for example, the methodological training the average social science major receives, particularly in comparison to students in the computational sciences, applied mathematics or various areas within the natural sciences, such as physics, it is not much.  For example, as a professor in the states, I have spent the past few years implementing advanced data and research methods courses into our undergraduate sociology program--by the way, I want to give a "shot out" to my Kent State Ashtabula students! 

Now, I must say, it has not been an easy endeavor.  But, to my surprise, I have found the students to be very receptive.  There is, however, a very specific reason why: I present to them a wider definition of quantitative method than just statistics.

If we, as social scientists, took a few minutes to look at what students on the other side of our campuses are learning, we would find that statistics is only a small part of it.  Quantitative method, today, is all about BIG DATA,  e-science, data mining, GIS analysis, machine/artificial intelligence, control theory, agent-based modeling, network science, information visualization, and so forth.  I can just keep going and going...  But, why would my social science students be interested in such things?  Because, even when lacking a background in such methods, they intuitively "get the point" of such methods.  Common now, think about it.  Our students, live, after all, in a wild new, electronic world, a virtual reality permanently integrated into their everyday reality, data streaming everywhere, 24/7, dynamic in a virtual time/space: smart cell phones telling them where their friends are at any moment, emails, texts, tweets, kicks, Google maps, endlessly streaming music, Pandora radio, electronic banking, iTunes, social media networks, LinkedIn, YouTube, online television and videos--on and on and on and on it goes.

Explain to me, what does statistics have to do with such things?  I will tell you, not much.  Now, I will say, in all fairness, students still need to learn statistics; and, as such, I make statistics a prerequisite to students taking more advances modeling strategies.  And, in my own work, I rely heavily on statistics, particularly factor analysis, multidimensional scaling and cluster analysis.

But, at the end of the day, students want (yes, I did say that word "want") to learn about  (even if they get only a sampling) the other incredible methods that computer scientists, applied mathematicians, computational scientists, e-scientists, physicists, geographers, and others are developing.  So, to such a desire to learn, I say Wow!, and a Double Wow!

And, such a desire to learn about these new methods goes to the point recently made by the UK sociologist and complexity scientist, David Byrne.  In a recent article, UK Sociology and Quantitative Methods: Are We as Weak as They Think? Or Are They Barking up the Wrong Tree?  Byrne, an expert in quantitative method, makes the case that, given the massively increased complexity of daily social life, statistics alone is just not going to "cut it," as they say.  We need to learn from the complexity sciences.  And, we need to bring the theoretical and empirical strengths of sociology and the social sciences to help with the epistemological and theoretical challenges of studying complex social systems. 

I agree wholeheartedly, and I think that students intuitively do as well.  If the social sciences seek to be on par with the methodological abilities of the natural and computational sciences and applied mathematics, they must understand that these disciplines--while facile in statistical analyses--have moved far, far beyond such methods.

In other words, we have a lot of catching up to do, and focusing on statistics, while necessary, is not sufficient.  Add to this fact the reality that, in the sciences and in the labor market our students will work in research teams comprised of groups and networks of peers from a variety of academic backgrounds, where cross-disciplinary communication will be vital to success, we have a great need to create a much larger, more methodologically sophisticated vocabulary and tool set for our students.