Non-Equilibrium Social Science

A major issue in quantitative social science today is dealing more effectively with, as Emma Uprichard calls it, our data saturated plastic present---her words for describing the world of endless digital data and its supporting cyber-infrastructure in which we now live.

In response, my colleague, Rajeev Rajaram and I have been developing the SACS Toolkit and our case-based modeling approach to include the tools of dynamical systems theory and non-equilibrium statistical mechanics---the former focuses on mathematically modeling complex systems across time and the latter on modeling the links between microscopic behaviors and macroscopic patterns.

Click here to see our most recent paper, which was published in Complexity.

Having said that, we were wonderfully surprised and very much excited to have come across a group of likeminded social scientists who likewise find the tools of non-equilibrium statistical mechanics and dynamical systems theory useful for modeling social complexity and complex social systems.

The website is called Non-Equilibrium Social Science.  It appears to be an interesting group of physicists, complexity scientists and economists, who are all over the place--involved in the private, public and academic sectors--and espousing a rather diverse set of scientific, economic and political views--not all of which anyone, including myself, will agree with.  But, that is the purpose of a good blog, to get people thinking, agree or not!  Besides, Joseph Stiglitz, one of my heroes, is on their science board, so that is very cool!

For me, I am mostly interested in their development of the tools of physics for studying and modeling the temporal and spatial dynamics of complex systems, particularly in reference to big data.

What I also very much like is how well-connected their website is to what is going on in the world of complex social science.  They have a twitter feed, archives, a blog, and so forth.  I particularly like, and have used in my classroom teaching, their SCOOP.IT account, which you have to check out!

Well done!  I look forward to learning more about this group.

Big Data, Big Data and More Big Data

As my colleague, Emma Uprichard, has pointed out in a series of recent articles, we live, today, in a data-saturated-plastic-present--for more on Uprichard's articles, click here.

The following article, 
------------------------------
Big Data Follows and Buries Us in Equal Measure

By Norman Ball 10 November 2013, , 

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which I have linked here, is a good example of how and why we have come to live in a world of big data and the challenges that come with this new plastic present. 

Complexity Map Wins Award for the Mapping Science

I am happy to announce that my complexity map was chosen to be one of the seven maps representing this year's iteration of the Mapping Science project.  The exhibit was envisioned by Katy Börner, Kevin Boyack, Sarah I. Fabrikant, Deborah MacPherson and André Skupin in January 2005.  Currently,  it is run by Katy Börner and Todd Theriault


Click here to see my map, which, as you might guess, was updated for the project.

As stated on their website:

Places & Spaces: Mapping Science is meant to inspire cross-disciplinary discussion on how to best track and communicate human activity and scientific progress on a global scale. It has two components: the physical part supports the close inspection of high quality reproductions of maps for display at conferences and education centers; the online counterpart provides links to a selected series of maps and their makers along with detailed explanations of how these maps work. The exhibit is a 10-year effort. Each year, 10 new maps are added resulting in 100 maps total in 2014.

Click here to see more about the Mapping Science project.

Data Stories: 25 Podcasts on Data Visualization by Enrico Bertini and Moritz Stefaner

Data Stories

A few weeks ago, my colleague and friend, Emma Uprichard, emailed me about a series of podcasts on data visualization that she ran across.  The series includes 25 podcasts in all, and are done by Enrico Bertini and Moritz Stefaner, who refer to the series as Data Stories--click here to see their website.

Each of the 25 podcasts is about an hour in length, usually involving some informal type of discussion between the two authors, or an interview with a colleague or expert in a particular area of data visualization.  Also provided are additional links and papers and related topics.

Overall, they are very informative and cover a wide range of topics.  A minor criticism: sometimes they wander a bit and can get sidetracked; also, sometimes they get a bit jargon ridden--the way your friendly computer department tech can---but this is a minor criticism.  In the end, I recommend them, especially to those new to these ideas and the data-side of the highly global, digital, visual world(s) in which many of us now live.

The mysteries of life and the cosmos are too complex, even for science; so humility please in all endeavors

Back in 2008, Stuart Kauffman, the world-renown complexity scientist and biologist, published a very interesting book, Reinventing the Sacred: A New View of Science, Reason and Religion.  Most have probably heard about it or even read it.  I am not religious or belong to any faith tradition, but I found it very interesting science.

For me, what makes it an interesting read is that it is classic Kauffman.

What Makes Stuart Kauffman So Brilliant

For me, Kauffman is a brilliant (and highly unique) scientist and scholar because he is always able to take the next step, intellectually, into ideas that seem, at first, incredibly odd or strange or just downright impossible.  A little later, however, as the rest of us come along, and as time goes by, we come to realize that, you know what, minor issues aside, he has a pretty good idea---with "good idea" meaning that it has proven scientifically useful. Perhaps the best example of this point is Kauffman's ground-breaking notion that self-organization is the other half of the evolutionary coin.  The other example---and the focus of my current post---is his book, Inventing the Sacred.

Reinventing the Sacred

Here (from its back cover) is a quick summary of the book's central theme:

"Consider the complexity of a living cell after 3.8 billion years of evolution. Is it more awesome to suppose that a transcendent God fashioned the cell at a stroke, or to realize that it evolved with no Almighty Hand, but arose on its own in the changing biosphere?  In this bold and fresh look at science and religion, complexity theorist Stuart Kauffman argues that the qualities of divinity that we revere—creativity, meaning, purposeful action—are properties of the universe that can be investigated methodically. He offers stunning evidence for this idea in an abundance of fields, from cell biology to the philosophy of mind, and uses it to find common ground between belief systems often at odds with one another. A daring and ambitious argument for a new understanding of natural divinity, Reinventing the Sacred challenges readers both scientifically and philosophically."

So, What is My Point?

Sorry for the delay in making my point, but the setup was necessary.  Whether you agree with Kauffman or not, I think he is making a more general point.  Or, at least, that is my read.  As a backdrop argument, I think he is saying that arrogance in science or religion will get us nowhere; and fighting amongst ourselves over the power to be "right at the expense of all other views," be it in religion, science or anything in-between, is destructive.  As Foucault said, polemics (in contrast to debate) are useless.

Case in point.  Over the past few months I have come across the following post (A review of Reinventing the Sacred) on at least a dozen or more occasion.  Best I can surmise, it was originally written by the British paleontologist, evolutionary biologist (and let us also not forget, Tolkienist), Henry Gee. 

While critical of Kauffman, Gee's point is my own--or maybe, my point is Gee's; that's probably better stated.  Actually, my point is Gee's point, which I also think is, as a backdrop, Kauffman's point.  It is a variation on what I just said above: C'mon folks, all those certain of their science or religion; drop the arrogance and show a bit more humility, please!  Kauffman may or may not be right.  So, let's debate the validity of his ideas, but drop the polemics.  Otherwise, you won't get invited to all the cool parties, as your such a 'debbie downer' conversation hog.

Here is Gee's post in its entirety:

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An argument that complex systems transcend natural law, and thus are symbolically sacred.

Reinventing the Sacred
A New View of Science, Reason and Religion
By Stuart A. Kauffman

In Unweaving the Rainbow, Richard Dawkins boasted that he once told a child that Santa Claus didn't exist. The argument was that Santa couldn't possibly visit all the world's deserving homes in a single night, quite apart from the physical difficulties of flying reindeer, narrow chimney stacks, and so on.

As well as illustrating the intellectual level of Dawkinsian discourse, this anecdote betrays a lack of knowledge of contemporary physics. Santa could do what he does quite handily, you see, if you consider him as a macroscopic quantum object - something that behaves according to the weird world of quantum physics but is large enough to be visible.

In such a guise, Santa could appear in as many places as he wanted to, simultaneously, without having to negotiate chimneys, provided nobody was watching. If he were caught in the act, his wavefunction - the probability that he might be everywhere at once - would collapse and he'd be revealed as your grandpa, after all.

And quantum effects are manifested at the macro scale only in extremely cold conditions, which explains why one routinely addresses one's Christmas list to Lapland or the North Pole, rather than, say, Brazil or Equatorial Guinea.

My Quantum Santa Hypothesis (QSH) works better than Dawkins' classical one because it explains the taboo about watching Santa at work, as well as his traditional location in cold climates - aspects Dawkins fails to tackle. The QSH explains more of the evidence in a single theoretical scheme than his does.

This is not to say that Santa exists, however. I have never challenged Professor Dawkins with the QSH. But the reaction of some of his acolytes to my original exposition (in the Guardian of Dec. 14, 2000) was predictable: Anyone who challenged Dawkins' view on this question was obviously a believer, and therefore not to be trusted.

This simplistic, with-us-or-against-us worldview is as deficient in subtlety as it is in humor. We know what we know because of science, it says. Science explains everything. So anything that falls outside that explanatory system must be false, illusory, even evil. What such defenders of science fail to see is that this line of reasoning betrays a dreadful misuse of the scientific method.

Theoretical biologist Stuart A. Kauffman, who taught at the University of Pennsylvania from 1975 to 1995, is unlikely to fall into that trap. In Reinventing the Sacred, he takes aim at reductionist reasoning, much used in the sciences. Reductionist thinking takes complicated systems to pieces, studies all the pieces in isolation, and then sticks them back together again. Powerful and useful. Kauffman argues, however, that reductionism fails to explain the properties of systems that are "emergent" - that come into being by virtue of their inherent complexity, and whose properties cannot be explained by reducing them to the simpler systems from which they arise.

Say you have a few pounds of carbon compounds and a bucket of water, and you know how these behave chemically. It's nevertheless impossible to predict that the combination of these substances might be capable of evolving into structures (human beings) capable of self-reflection: Cogito ergo sum. Darwinian adaptations, agency, awareness, economics and human history are all emergent, and cannot be reduced to what Kauffman calls the physicists' system of "particles in motion."

Caution: This is not the same thing as the "irreducible complexity" that the intelligent-design camp claims is a sign of the hand of God. Such is no more than politically motivated special pleading. Instead, Kauffman goes to great lengths to suggest, in intense detail and with a rigor that, frankly, takes no prisoners, how emergence arises.

The message in chapter after chapter is that any reasonably complex system - whether the global biosphere or human technological ingenuity - betrays a "ceaseless creativity" that transcends fundamental natural laws and requires no prime mover.

Kauffman's reasoning is, in the main, faultless. It falls down, however, in two places. The first is his proposal that consciousness is based on the quantum mechanical properties of cellular substructures. Some recent work does show that certain proteins, in the dense milieu of cells, can manipulate electrons Santa-fashion, keeping all quantum possibilities open for as long as possible.

This idea is fascinating, but Kauffman appears to speak as if such properties were confined to neurons in the brain. Nowhere does he explain why they should not exist in other kinds of cell - a flaw that exposes him to accusations of arguing that brain cells are somehow exceptional. By the same token, he dismisses, out of hand, the idea that "mind" might be an emergent property of the trillion-fold interconnectedness of billions of neurons - a casual swipe that goes against everything else he says in the book about complex systems.

The second failure is the whole God business. The concluding chapters are more readable than the rest (in a book that is often an eye-watering challenge to read), but they degenerate into a repetitive mantra in which Kauffman says that the "ceaseless complexity" of the world, while not being evidence for a Creator God, should somehow be "symbolic" of God, or, at least, of something "sacred." He cannot prove this logically, he says; he can only try to persuade us.

This appeal to a kind of primitive pantheism is both sincere and charming, but in the end it is simply more special pleading. The fact is that in Kauffman's scheme, God is unnecessary, even if reductionism fails, so in the end one wonders about the point of preserving a sense of God.

To be sure, certain scientists could surely use a dose of humility before the evidence. Science cannot explain why human beings act and feel and think in the way they do in specific circumstances, and spirituality might even be important, valuable and worthy of respect. But what does God have to do with any of this?

I'm hedging my bets - I'm asking Santa for a quantum computer for Christmas.

Henry Gee is a senior editor of the science magazine Nature. 

New Version of Complexity Map---The Complexity Map Version 5

Hello everyone!  As you can see above,  I have (once again) updated my map of the complexity sciences.

CLICK HERE TO SEE THE INTERNET VERSION OF MY MAP

CLICK HERE TO DOWNLOAD A HIGH RESOLUTION PDF VERSION OF THE MAP

To cite this map use the following

Castellani, Brian 2013. Complexity Map Version 5.  Sociology and Complexity Science Blog. http://sacswebsite.blogspot.com/2013/07/the-complexity-map-version-5.html.

Complexity Map Version 5

Complexity Map Version 5 is a massive update, based on my continuing attempt to keep the map as useful as possible to an ever-growing field and audience.  For Version 5, I went back to the beginning, as they say, trying to "fill in" the map and its major trajectories by:

1. Breaking larger areas of study (such as social complexity or the dynamics of complex systems) into sets of smaller but interconnected areas of research.
2. Adding newer or smaller areas of study that have stabilized into identifiable fields of scholarship, such as computational biology, visual complexity or data science.
3. Adding more scholars (both major and minor) to reflect the widening depth of the field.  

Reading Complexity Map Version 5

This map is a macroscopic, trans-disciplinary introduction to the complexity sciences.  Moving from left to right, it is read in a roughly historical fashion, evolving along the field’s five major intellectual traditions: dynamical systems theory (purple), systems science (light blue), complex systems theory (yellow), cybernetics (grey) and artificial intelligence (orange).  Placed along these traditions are many of the key scholarly themes in the complexity sciences.  A theme’s color identifies the historical tradition with which it is best associated, even if a theme is placed on a different intellectual trajectory.  Themes in brown denote discipline-specific topics, which help illustrate how the complexity sciences are applied to different content. Double-lined themes denote the intersection of a tradition with an entirely new field of study, as in the case, for example, of visual complexity or agent-based modeling.  Connected to themes are the scholars who founded or exemplify work in that area. 

Mapping Science: A Few Lessons Learned

I learned a few lessons about the challenges of mapping the history of science.

First, as Foucault says, there are only histories of the present.  History is largely a backward looking profession, charting the movements of things across time and place from the present position of the historian.  For example, I remember my father-in-law, Len Rusnak, saying that the further away we get from certain presidents in the states, for example, such as Truman, the better or worse they start looking, depending upon the lines of influence we are drawing from them to the present.  The same seems to be true of science.

I have noticed over the years that, as new area of study emerge within the complexity sciences, new historical lines of influence are established, new scholars emerge as more or less important, and new historical lineages are developed.  Case in point.  If you go back to the reviews of complexity written in the late 1990s, the emphasis was, historically speaking, almost entirely on systems science, cybernetics and the work taking place at the Santa Fe Institute, in New Mexico, USA.  More recently, however, lots of smaller and more specific histories have emerged.  In the social sciences, for example, ties are being made to all sorts of epistemological positions, from postmodernism to poststructuralism to constructionism to critical realism.  And, while a scholar like Per Bak and his work on self-organized criticality, for example, was a "massively major" field of study back in the 1990s, he and his work have receded into the background, as new areas and scholars have come into the picture, so to speak, and have taken over.  As a result, the old stories have receded or softened a bit, turning into a more complex and nuanced storyline.  In response, I have found myself having to constantly evolve, develop and adapt the complexity map. 

Second, it seems that, given how wide-reaching the field is now, everyone has their own personal standpoint on the history of complexity.  I am constantly told, for example, that the complexity map, "while useful, is incomplete!" or that, "while it gets at most of the major stuff, it is a partial view of just one person!"  What?  Of course it is!  Have you not read anything on the philosophy or epistemology of complexity, going all the way to the early scholars developing the field of cybernetics and systems science?  The big, big point made by all these scholars is that all maps, models and theories of complexity and its interdisciplinary study will be, by definition, incomplete!  I mean, we are talking about an approach to science that, as Stephen Hawking and others have suggested, will most likely become, in the next fifty years, the dominant definition of science, with the word "complexity" simply being dropped.  So, C'mon folks!"

Given such realities, what is the goal of the current map?  While it strives to be reasonably exhaustive and impartial, it ultimately strives to help people into the field, to give them a broad understanding of many of its key fields of study and important scholars, pointing them in a variety of directions which they can explore further, drilling down, as they say, into finer and finer levels of analysis.  Or better yet, giving them the tools to develop their own maps, their own networks of connections and so forth.  It would be interesting, for example, to give people only the names and areas of study on this map and see how they arrange them.  I am sure that, while common patterns of arrangement would emerge, major differences would exist, never to be resolved.

Third, it is clear that, far from slowing down, the complexity sciences are advancing at an incredible speed, as this field's various approaches to modeling the topics of science are taken up across the academy!  It is very exciting to watch and map this progress, as the work scholars are doing is just incredible!

So, let's think of this map as an evolving dialogue (with the appropriate paper-trail) if you will: a debate, an argument, or (better yet) a charted negotiated ordering that has emerged through our complex interactions with the larger scientific history of which we are a part.  As such, I am sure that Version 10 of the map, to my own detriment (Ha!), is not too far off in the immediate future.  phew!

Cheers! 

  






 




  




 


 

social science departments need to innovate the teaching of method by embracing new approaches to big data, statistics, computational modeling and complexity

The title of this post says it all: social science departments need to innovate the teaching of method by embracing new approaches to big data, computational modeling and complexity.


As I discussed in a previous post---CLICK HERE---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.

In response, a variety of UK scholars from different disciplines and areas of study are innovating the teaching of method.  One major area of advancement, which I address on my map of complexity, is data visualization--a whole new field of study that intersects data mining, art, design, web science, computational science and so forth.

For example, I am on a UK listserv for teaching method and came across the following SEMINAR that was held on teaching data visualization.  Here is how they describe the even on the website for the seminar, which includes two of the keynote presentations:

Background

The Department of Social Sciences at Loughborough University is currently undertaking a pedagogical research project, sponsored by the ESRC, which involves introducing a new 22 week quantitative data analysis module for first year criminology and sociology students. The module emphases visual learning and teaching strategies and resources. It also assesses students using a portfolio of achievement rather than relying solely on the more traditional ‘statistical report’ format. The new module will be delivered for the first time in the academic year 2013-14 at undergraduate level 1.The objectives of the HEA sponsored workshop are to discuss the progress of this project as well as to explore more generally the types of visual learning and teaching strategies used to teach research methods and quantitative data analysis across Social Science departments in the HE sector.

Time to Play Catch Up

Social Scientists in the states and elsewhere need to "get on-board" as they say, with these types of advances taking place in the UK and elsewhere.  And (this is key) not just at the elite institutes.  These sorts of innovations need to be taking place at "anywhere and everywhere" colleges and universities and community colleges and technology schools.  We live in a world, now, where virtual and physical reality are almost entirely blurred.  Mean, median and mode and a few bar graph charts don't "cut it" anymore as effective techniques for measuring and presenting this reality.  C'mon folks, there are lots of exciting things happening and we need to share them with our students!!!!!!!

Here is a list of a few examples to explore on the topic of data visualization and data science:

Places and Spaces: Mapping Science project, run by Katy Börner and Todd Theriault.

A good introductory article summarizing the field

A Wikipedia article introducing the field of data visualization

A Wikipedia article introducing data science and big data

A website by one of the top people in the field, on visual complexity

A great interactive map, found at TIME Magazine, of the USA and population sizes by cities.

A good review of two recently published books on visual complexity

The Complexities of Space and Place

I remember, back in the days of postmodernism, a colleague, a geographer, was giving a talk on postmodernism and geography.  A few of my colleagues commented, saying something like, "What does geography have to do with pomo?"  Turns out, the answer was a lot: narratives, national identity, notions of the globe and global, the epistemology of space and place.... It just keeps going.

A few years back I went to see the same colleague, my geographer friend, lecture on complexity and geography.  Same response from colleagues, albeit even more ignorant: what is all that complexity stuff and what does it have to do with geography?  Turns out, the answer is, again, a lot: residential mobility and Schelling segregation, networks and the dynamics of space and place within them, global network society, Big Data and geospatial analysis, smart phones and Google maps, mapping disease spread, social mobilities, the blurring of spatial boundaries, the geography of the internet....  Again, it just keeps going.

In fact, in my mind, one of the most exciting new area of analysis in the complexity sciences today is the complexities of space and place.  One of my students, in fact, who just graduated with his bachelors, is heading on to study complexity and geospatial analysis, and he majored in sociology, with an emphasis on health and health care.

In his mind, and in mine, this is "where it is at in medical sociology," in many ways--from the sociology of population and community health to epidemiology and the geography of health and wellbeing to the built environment and urban planning to health behaviors and networks.  And, don't forget all the methods, from agent-based models to networks to GIS software.

There are so many people to mention and lots of websites and new centers and areas of research to highlight.  Impossible for a quick blog.

Here, however, are a few to get you going:

Michael Batty and the complexities of cities

Centre for Advanced Spatial Analysis

Nigel Thrift and the geography of complexity

David O'Sullivan and geography and complexity science

Barabasi and colleagues on mobility in networks

John Urry and social mobilities

Manuel Castells and global network society

Complexities of place and health


lots and lots of stuff.  very exciting work.

Complexity Map Translated into Portuguese

Complexity Map Translated into Portuguese

My complexity map was recently translated into Portuguese by Rosângela Ap.

Rosângela Ap is a member of the research group GIIP - Group International and Inter-Institutional Research Convergences between Art, Science and Technology at the Art Institute of Universidade Estadual Paulista Júlio de Mesquita Filho in Brazil and teaches art.  Her work, as an artist and researcher, explores the intersection of art and technology and, more recently, complexity.

Ap and colleagues presented their translation of the complexity map recently at the 3rd International Research Group: "Mixed Realities & Convergences between Art, Science and Technology"

To contact her about the map or related work,
here is a link to her website (including her recent art) and here is a link to her blog.   Also to UNESP and to GIIP.

Punching Clouds; Or, Complexity Science Meets Public Decision Making

I just finished reading through two excellent books by Lasse Gerrits.

Gerrits is an associate professor at the Department of Public Administration at the Erasmus University Rotterdam. He studied Policy and Management of Complex Spatial Developments and was a researcher at TNO Built Environment and Geosciences.  He also has an excellent but now terminated blog on the links between urban life and complexity.  It is called Cityness.

He received his Doctor’s degree in 2008 and is a "fast-rising" scholar in the complexity of public policy and public decision making.  His two new books demonstrate why.

Before turning to those books, it is important to also note that Gerrits is likewise making a name for himself in the growing network of research in which my own work is situated, namely case-based complexity science and its more specific domains of inquiry: case-based modeling and David Byrne's complex realism.  To see Gerrits' work in these areas, click here.  Now, back to the books.

Punching Clouds: An Introduction to the Complexity of Public Decision-Making is an excellent introduction to the massive, burgeoning literature on the complexities of public policy and public decision making.  What makes it particularly useful is that it (a) bridges the gap between theory and application and (b) advances the empirical rigor and theoretical organization of the field.

To date, the formal, scientific literature in the complexity sciences has not been well integrated into such applied fields as city planning, public policy and the managerial sciences, as these 'applied' fields have tended to take a more pop-science and metaphorical approach to complexity--which can be rather problematic.  Why?  Because it is not often clear if (a) the concept of complexity are being used correctly or (b) the authors are actually saying anything new.

On this note, it is worth pointing out that Kurt Richardson of Emergent Publications and the E:CO journal published this book.  Richardson and colleagues, such as Paul Cilliers (in memoriam) are known for the high quality of their work.  And so it is with the current book by Gerrits. 

Punching Clouds cuts through the pop pretenses of much of the applied literature by taking a measured approach to complexity.  Gerrits, for example, states: Unlike some authors, I do not see the complexity sciences as presenting a revolution of thought. Rather, its main value for me lies in the fact that it provides a fairly coherent framework for integrating various ideas, including those that predate the complexity sciences, in a way that helps us gain a deeper understanding as to why public decision-making is such a complex subject. The key for this lies in how complexity deals with time and causation. As such, this book is a first but not final attempt to make sense of the complexity of public decision-making.

The payoff of this approach is massive.  For example, I have spent the past several months just meditating on the first several sentences of the book, as they are both provocative and yet intuitively correct.  Gerrits states:

Much of the work in the public sector is fairly simple, unchangeable and predictable. A minor part of the work isn’t. Although seemingly small, this complex portion requires much of peoples’ time and energy, and presents often unpredictable results. A fatalistic response to this complexity could be to give up and to go home. A different response would be to make an effort at understanding this complexity. This book presents such an attempt.

Wow!  These sentences ring true for so many public policy issues.  In my particular area of study, public and community health, I started thinking about some of the major problems we face here in the states: health poverty traps, people going bankrupt over health care costs, food deserts, unhealthy built environments, disease transmission through health networks, the failures of preventive care.  These issues remain, entrenched, despite efforts to fix them, due in large measure to a failure to understand their complexity.  It is, as the title of the book states, like punching clouds.  Anyway, I can go on but I will stop.  Get the book.  I highly recommend it!  I also recommend Gerrits' edited book, which very much functions as a companion to Punching Clouds.

COMPACT I: Public Administration in Complexity is Gerrits' second book, edited with Peter Marks.  Here is the summary as they present it:  There is an argument that says that research in Public Administration is always about social complexity. This argument is true. There is also an argument that says that Public Administration is actually very little informed by complexity. This is equally true.  The differences lie in the different takes on complexity. The latter approach understands that comprehension of complexity requires a specific theoretical framework and associated tools to look into the black box of causality.

The authors in this edited volume gathered in Rotterdam (The Netherlands, June 2011) to discuss how the complexity sciences can contribute to pertinent questions in the domains of Public Administration and Public Policy.  Their contributions are presented in this edited volume. Each contribution is an attempt to answer the Challenge of Making Public Administration and Complexity Theory work-COMPACT, as the title says. Together, they present an overview of the diverse state of the art in thinking about and research in complex systems in the public domain.

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In conclusion, as Gerrits states himself, the complexities associated with public policy and public decision making are beyond quick fixes or final solutions.  But, if we are to make some progress with them, then understanding correctly their complex nature is the first and most important new step.

 

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.