A New Law for Complex Systems! Past the Power Law: The Limiting Law of Restricted Diversity and the 60/40 Rule

Over the past year we have worked on a series of articles which seek to provide a new way to measure the distribution of diversity in complex systems.  The result is a new way to measure complexity, called case-based entropy.  We published an article on this measure in Physica A.

In turn, we used this new measure to uncover a new limiting law that we found hidden in plain sight in a wide variety of skewed-right complex systems.  We call this limiting law restricted diversity, arguing that this law governs the distribution of diversity in skewed-right complex systems the same way the law of central tendency governs the distribution of diversity in normal distributions.

Even more compelling, restricted diversity can be rigorously measured in ways that far exceed the limits of the power law, revealing a 60/40 rule for skewed-right complex systems.  We just published these second two insights in a article in Complexity.

Below are the abstracts and links to the two papers:

Past the power law: Complex systems and the limiting law of restricted diversity

Probability distributions have proven effective at modeling diversity in complex systems. The two most common are the Gaussian normal and skewed-right. While the mechanics of the former are well-known; the latter less so, given the significant limitations of the power-law. Moving past the power-law, we demonstrate that there exists, hidden-in-full-view, a limiting law governing the diversity of complexity in skewed-right systems; which can be measured using a case-based version C of Shannon entropy, resulting in a 60/40 rule. For our study, given the wide range of approaches to measuring complexity (i.e., descriptive, constructive, etc), we examined eight different systems, which varied significantly in scale and composition (from galaxies to genes). We found that skewed-right complex systems obey the law of restricted diversity; that is, when plotted for a variety of natural and human-made systems, as the diversity of complexity --> infinity (primarily in terms of the number of types; but also, secondarily, in terms of the frequency of cases) a limiting law of restricted diversity emerges, constraining the majority of cases to simpler types. Even more compelling, this limiting law obeys a scale-free 60/40 rule: when measured using C, 60%(or more) of the cases in these systems reside within the first 40% (or less) of the lower bound of equiprobable diversity types—with or without long-tail and whether or not the distribution fits a power-law. Furthermore, as an extension of the Pareto Principle, this lower bound accounts for only a small percentage of the total diversity; that is, while the top 20% of cases constitute a sizable percentage of the total diversity in a system, the bottom 60% are highly constrained. In short, as the central limit theorem governs the diversity of complexity in normal distributions, restricted diversity seems to govern the diversity of complexity in skewed-right distributions.


An entropy based measure for comparing distributions of complexity

This paper is part of a series addressing the empirical/statistical distribution of the diversity of complexity within and amongst complex systems. Here, we consider the problem of measuring the diversity of complexity in a system, given its ordered range of complexity types i and their probability of occurrence pi, with the understanding that larger values of i mean a higher degree of complexity. To address this problem, we introduce a new complexity measure called case-based entropy  Cc — a modification of the Shannon–Wiener entropy measure H. The utility of this measure is that, unlike current complexity measures–which focus on the macroscopic complexity of a single system–Cc can be used to empirically identify and measure the distribution of the diversity of complexity within and across multiple natural and human-made systems, as well as the diversity contribution of complexity of any part of a system, relative to the total range of ordered complexity types.


New Case-Based Complexity and Case-Based Modeling Website

Our development of the new approach to modeling complex systems, known as case-based complexity, is gaining momentum.  My colleague, Rajeev Rajaram and I, for example, are already close to 30 papers on the topic.  But, as with most article publishing, they are scattered across a wide variety of journals.


So, after repeated inquiries from colleagues for copies of our various papers, I thought it useful to create a website that included all of the papers my colleagues and I have written on the topic of case-based complexity and, more specifically case-based modeling, including a basic introduction to the concepts.


New 2016 Version of Map of the Complexity Sciences

Hello all,
I have yet another new 2016 internet version of my map of the complexity sciences ready to go!  Lots more names and several new fields added.  Hope you enjoy!  And, it is all online with links to the internet!


See my recent article in THEORY, CULTURE AND SOCIETY journal where I reflect on my fifteen years as a complexity scientist.

The article includes my TOP TEN things about complexity you may not know but should!


A Review of Capra and Luisi's The Systems View of Life: A Unifying Vision

As of 2014, there has been much written in the complexity sciences on the all-purpose topics of complex systems and networks and their related scientific methods – I am thinking here, for example, of Byrne and Callaghan’s excellent “Complexity Theory and the Social Sciences” or Mitchell’s “Complexity: A Guided Tour.”

What really hasn’t been written, however, is a cohesive or comprehensive review of the content (the actual empirical outcome) of this cutting-edge research – which, in almost every way imaginable, is creating an entirely new view of human life and the global ecosystem that sustains it.

Enter Capra and Luisi’s new textbook “The Systems View of Life.”

For those new to the complexity science literature (or professors thinking about adopting this book for class), one couldn’t ask for a better writing partnership.  Capra, a physicist by training, is world-renown for this twin books on systems and complexity science (“The Web of Life” and “The Hidden Connections”), as well as his provocative assessment – from a philosophy of science perspective – of the limits of conventional, mechanistic science and the need for a new, holistic, ecologically responsible systems science (“The Tao of Physics,” “The Turning Point” and “The Science of Leonardo”).  In turn, Luisi is an internationally recognized professor of biochemistry and complexity science, having done primary research into such core issues as cellular autopoiesis and synthetic biology.  He is also well known for his in-depth academic books, as well as his two popular works, “The Emergence of Life” and “Mind and Life.”

Divided into three parts, “The Systems View of Life” is a compendium of all-things systems thinking and complexity science:

Part 1 (sections 1 and 2) is devoted to the philosophy of science, focusing on the historical shift from mechanistic thinking (dominated by reductionism, Newtonian mechanics, social physics and a Cartesian view of life) to systems thinking (dominated by the holism, networks, nonlinear mechanics, global network society, and a complex systems view of life).  Capra and Luisi are clear: mechanistic thinking is a victim of its own success, as it was so powerful in solving so many issues over the last hundred or so years that (now) it is simply assumed, almost by definition, that it can solve all current problems, which is wrong, as the problems of today, as Warren Weaver pointed out all the way back in 1948 (Science and Complexity), are complex systems problems.

For professors thinking about this textbook, Part1 is an important addition to the literature – here I am thinking of Hammond’s “The Science of Synthesis” and Klir’s “Facets of Systems Science” – as Capra and Luisi's chapters provide the historical backdrop missing from most introductions to the complexity sciences, helping students, as I already alluded to, understand why the sciences are shifting.

In Part 2 (the third section of the book), Capra and Luisi venture into entirely new territory, doing something (as I have already suggested in my opening remarks) yet to be done in the literature, let alone a textbook: they synthesize the empirical insights of the systems and complexity sciences into a new and cohesive view of life.  As they state in their introduction, “We present a unified systemic vision that includes and integrates life’s biological, cognitive, social, and ecological dimensions.”

The accomplishment of this task cannot be underestimated, as it is significant and should have a lasting impact, demonstrating just how visionary the complexity sciences can be – but only if time is given to their study (I am also thinking of students here) and to collecting and connecting up their insights.

Such a synthesis requires, however, a bit more effort than just connecting the dots – even though Capra and Luisi humbly suggest that this is all they are doing.  Instead, it requires a theoretical frame, which the individual empirical insights often lack.

For Capra and Luisi, the theoretical frame is a network-based view of life.  Networks provide, literally, the links from one topic to the next in their book, in a sort of “scale-free approach to knowledge,” where one moves freely from the human genome and human cognition to social organizations and cities to ecosystems and global society.

But, this is not where things end.  For Capra and Luisi, these links must extend beyond theory and empirical synthesis to application and policy – to helping the world become a better place, to the moral culpability of science and to doing the right thing!

While not by any means unanimously embraced, there is a global morality associated with a significant segment of the systems and complexity science community, which goes by a variety of names, from deep ecology and ecofeminism to post-humanism and global civil society.  Regardless of the term, the view is the same: we face, currently, as a global society, a significant number of complex systems problems, which can be better managed (or even solved) if the political, economic, scientific and public will to employ such a perspective exists!  If not, these problems will most likely be our doom – or, less dramatically, they will result in increased global disparity and inequality and, ecologically speaking, a significantly degraded and decompensated planet.

And so, in the final section of the book – Part 3 – Capra and Luisi employ the complex systems view of life to make sense of and, in turn, address the current list of global social problems we, as a global society, face: from population growth and climate change to economic sustainability and the development of a global civil society.

Again, for a science textbook, this is new territory.  Professors typically do not challenge students to think about the links between their science and the global world in which they live.  But that is, nonetheless, where our immediate future resides: we need our students, as the generation that will inherit all of these problems, to have the tools necessary to address them, and in a way that leads to a sustainable level of economic, political, cultural, and spiritual/existential wellbeing for the greatest number of people possible!  What more, in 500 pages or less, could a professor (or our students) want from a book devoted to making sense of the complex lives we currently live?   And so, whether you are teaching introduction to sociology or macroeconomics, cognitive psychology or cultural anthropology, microbiology or philosophy, it doesn’t matter; make this textbook part of your required reading list.

Our future depends upon it.


"Intersections: Brian Castellani and Dante Rodriguez" Art Show June 20th – July 18th CWAL Gallery, Cleveland

I have an upcoming art show with a colleague of mine at the CWAL Gallery, located in the new 78th Street Galleries – a large space, with over 40 art galleries,  which is part of the newly renovated Gordon Park Art District on the west side of Cleveland http://www.gordonsquare.org/.  

It would be great to get folks to support the Cleveland art scene!!!!!! 
Here is information about the show – in addition, I have attached the show’s flyer to this email
Intersections: A Show by Brian Castellani and Dante Rodriguez
June 20th  – July 18th  CWAL (Cleveland West Art League)
Opening Reception, Friday June 20th – 6pm to 10pm
Closing Reception, Friday July 18th – 6pm to 10pm
and open by appointment
LOCATION: 1305 W. 80th Street, First Floor Suite 110, Cleveland Ohio, 44102
BRIAN CASTELLANI: Here is a link for folks to see some of the art I will be showing:
DANTE RODRIGUEZ: Here is a link for folks to see some of the art by my colleague:


Systems and Complexity Sciences for Healthcare Conference (Nov 13th and 14th, 2014)


Working with Joachim Sturmberg as our lead, my colleagues and I are hosting one of the first international conferences on Systems and Complexity Science for Healthcare this November, the 13th and 14th, at Georgetown University!  -- click here to learn more or to register!!!!!!

It is going to be a fantastic conference, with opportunities for poster presentations and lectures by a handful of the top scholars in the field.  Also, the proceedings will be published by Springer


Early bird registration ends on September 20, 2014
Submission Closure for Abstracts: June 15th 
Full papers need to be submitted by September 15th

The conference is the result of the overwhelmingly positive response that colleagues and readers around the world have had to Sturmberg and Martin's Handbook of Systems and Complexity in Health -- a cutting-edge compendium of chapters on just about every major topic at the intersection of health care and the complexity sciences.

Here is the formal invite for the conference:

The prevailing 20th century explanatory models in health, health care and health professions education are no longer fit for purpose. Simple interventions like antibiotic therapies and vaccination have made significant contributions to reduce the premature mortality rates due to infectious disease.  However, healthcare in the 21st century is confronted with the global systems-based challenges from chronic disease to lifestyle illnesses and global disease transmissions. The etiology of these health problems is complex and simple interventions no longer suffice in dealing with them. Systems and Complexity Sciences offer a different model to understand and manage these emerging challenges.
An international conference will bring together for the first time leading thinkers and researchers to explore and exchange insights under the theme: The value of systems and complexity sciences for healthcare: An imperative for the 21st century.
The conference will offer plenary sessions, oral and poster presentations as well as interactive sessions. All presentations will showcase how systems and complexity sciences contribute to providing better care and achieving more desired patient health outcomes.
The conference will create an active engagement between all participants and foster networking. It will encourage interdisciplinary research and collaboration amongst an international complex systems thinking community.

Conference proceedings will be published by Springer thereby expanding further the reach of the meeting’s shared insights to the broader practice, research, education and policy communities.


The Limits of Social Engineering: A Complexity Science Critique

There is a new book out by Alex "Sandy" Pentland.

As most know, "Pentland directs MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program and co-leads the World Economic Forum Big Data and Personal Data initiatives. He helped create and direct MIT’s Media Laboratory, the Media Lab Asia laboratories at the Indian Institutes of Technology, and Strong Hospital’s Center for Future Health" (Amazon.com).

I locate Pentland and his work on my map of complexity via visual complexity and data science--Click here to See.

The title of Pentland's new book is Social Physics: How Good Ideas Spread -- The Lessons from a New Science.

It is a fascinating read and full of very provocative and interesting ideas, to which a number of reviewers have responded very positively.  I agree with these reviews that there is a lot that can be learned from this book  -- but I think these lessons come in the form of both possibility and also humility.

Given that so many reviewers have focused on the possibility component, let me speak to the humility part.

While a bit of history is provided in his book, the term social physics has a much deeper history than Pentland gives credit, going all the way back to the philosopher and scientist August Comte, and even a bit further to the scholar who first coined it, Adolphe Quetelet.  Like Pentland, these figures had similar ambitions for a new science of society.

Of the two, however, the terminology of Quetelet would fall into disuse.  Do not feel bad, though, as his ideas would be revived in the 1940s and 1950s, through the invention of the new fields of systems science and cybernetics, which would lead to second-order cybernetics and such famously exciting and provocative, but disastrously overreaching works of great genius as Norbert Wiener's The Human Use of Human Beings.  

These works were exciting and provocative because they hailed (albeit pessimistically so) the new information-driven, post-industrial, global network society in which we now live, grounded in a complex intersection between humans and machines (cyberinfrastructure)--click here for more!  Wiener states:  "It is the thesis of this book that society can only be understood through a study of the messages and the communication facilities which belong to it; and that in the future development of these messages and communication facilities, messages between man and machines, between machines and man, and between machine and machine, are destined to play an ever-increasing part." (p. 16)

But, they were disastrously overreaching, as they placed the future understanding of humans in their own machinery.  In other words, despite their best inventions, which included embracing a complex systems view of life, they failed to recognize, in their hubris, the shear complexity of the human condition, no matter how sophisticated the machinery!

Nothing, however, was going to slow down such technological innovation.  And so the world changed.

In fact, as our information-driven society evolved, the work of Wiener and others would likewise evolve, through their intersections with a wide variety of fields and innovative thinkers, to become the new complexity sciences -- which are fast becoming simply the sciences!!!  Again, the challenge of these new sciences will be, in part, how well they learn the lesson of limits.

Oh, by the way, if Quetelet's terminology evolved (eventually) into the complexity sciences, what happened to Comte?  He went on to invent the other BIG social physics (science) devoted to uncovering the underlying and complex patterns of socio-bio-physical organization that govern the social behaviors of humans, individually and collectively!  We call it SOCIOLOGY!


Unfortunately, such a complex history (including its abject failures and the warnings of limits) is absent from Pentland's book.  But, the same hubris is there.  For example, if one goes back to the writings of Comte and Quetelet, one finds the same sort of over-reach that one finds in Pentland's book, as if these scholars had written the preface to his 2014 work.

Here, for example, is a blab about Quetelet and his new social physics on Wikipedia:
Quetelet was among the first who attempted to apply it [statistics] to social science, planning what he called a "social physics"….  His goal was to understand the statistical laws underlying such phenomena as crime rates, marriage rates or suicide rates. He wanted to explain the values of these variables by other social factors. These ideas were rather controversial among other scientists at the time who held that it contradicted the concept of freedom of choice….  His most influential book was Sur l'homme et le développement de ses facultés, ou Essai de physique sociale, published in 1835 (In English translation, it is titled Treatise on Man, but a literal translation would be "On Man and the Development of his Faculties, or Essays on Social Physics"). 
In 1872, E. B. Taylor wrote a review of Quetelet's work--Click Here.  It is a fascinating review, as it foreshadows many the exaggerated hopes and claims that would be made by the "emerging sciences of the human condition" that came into existence during the 19th and 20th centuries, from psychology and sociology to anthropology and history to cybernetics and systems science to -- today -- complexity science and (once again) social physics.

But, again, what all such claims lack is a sense of humility -- and one grounded, importantly enough, in the capacity for self-critique, which fields like sociocybernetics discovered forty years ago!  As Byrne and Callaghan point out, there are useful and not so useful ways to model social complexity, and an uncritical social physics can fall into the latter.

The need for self-critique and for a thoroughgoing explore of what does and does not work when modeling the complexity of big data, is why I very much enjoyed a recent review of Pentland's new book by Nichlos Carr -- writing for the MIT Technology Review.  Carr's piece is called The Limits of Big Data: A Review of Social Physics by Alex Pentland.

While acknowledging the potential of the-new-yet-not-so-new, in fact not-new-at-all science of social physics,  Carr is skeptical of its claims.  Big Data and Big Science are Big Ideas, that is for sure.  And, we do live in a data saturated present, as Uprichard claims.  But, as Uprichard also points out, Big Data does not necessarily lead to Big Ideas.  Worse, like so many before it, Big Data can lead to Big Bad policy.  In other words, as Byrne and Callaghan point out, while the new tools of data science and computational modeling and complexity science show great promise, there is no guarantee that more data or new fancy tools means better answers -- as, at best, we only study the … traces … of the complex systems we seek to model.

So, yes, we live in a data saturated present, with big data everywhere; and, yes, social scientists, working with interdisciplinary teams, need to find the best and most effective tools for making sense of all this data --which the sociologists, Savage and Burrows, for example, have discussed.  But, for me, it needs to be done in a rigorously critical manner, and with more than a grain of humility.

That is why I like this poster, which my wife, a librarian, is always fond to point out:


The Limits of Statistics, Yet Another Example. Will Anything Chage?

Sometimes it seems that--in contrast to Kuhn and the notion of paradigm shift--no matter what is done to advance the state of things, the aggregate remains entrenched in convention, unable to wiggle free to the better idea.  Case in point: Bayesian statistics, computational modeling, big data science, topographical neural nets, viral computing, network analysis, mutli-agent modeling, complexity methods, applied mathematics -- where are the social sciences?

Anyway, here is yet another excellent thought piece on the limits of statistics for economic inquiry--CLICK HERE.  It is by Nassim Nicholas Taleb, Distinguished Professor at NYU.  Disagree or not, it is a good read.


Case-Based Complexity, Nonlinear Statistical Mechanics, and Modeling the Dynamics of Temporal Social Complexity

Check out an EARLY VIEW of our new case-based complexity article in Complexity.  It is third in a series of papers outlining the mathematical and theoretical basis to our development of a case-based density approach to modeling temporal and spatial complexity in social science data--which draws on the latest advances in nonlinear statistical mechanics, deterministic modeling, the advection equation, genetic algorithms, and other computational techniques.



BIG DATA, little questions!

Check out the following article by Emma Uprichard on Big Data! 


It is a great essay, as it points to the important and critical questions that folks, as of late, are not asking in real, sociologically critical ways, about the challenges and dangers of big data!  As always, as we learn in our Researching Methods courses, our modes of inquiry (and, equally important, the data and the frameworks upon which they are based) are only as good as our critical reflection on them, both in terms of their strengths and weaknesses, and in their links to theory and concepts!

The above image (a mapping of Twitter) comes from the Social Media Research Foundation--very cool place.  Click here for an interactive version of this twitter map!


Videos, Videos and More Videos on Complexity, Agent-Based Modeling and All Things Complex

 My colleague, Emma Uprichard, recently sent along the following link to a compendium of videos on all things complexity science and comptuational modeling. 

They are from the MASS website, shown to the right here.  They are fantastic! 

You should check them out.


My only critique is, please get rid of the dark background, as it is a killer on the strained eyes of us old-folks!!!


Big Brains. Small Films. Benoît Mandelbrot, The Father of Fractals

My friend and colleague, Frederic Hafferty (at Mayo) sent me this video on Mandelbrodt.  I very much like it, as it is short and sweet and very inspirational, especially when trying to get my undergraduate students to understand the beauty of mathematics and its scientific application.


Here is what the Youtube Video link said about the interview:

"Published on Nov 18, 2013
IBM and http://IBMblr.Tumblr.com celebrate the life of Benoit B. Mandelbrot, IBM Fellow Emeritus and Fractal Pioneer. In this final interview shot by filmmaker Erol Morris, Mandelbrot shares his love for mathematics and how it led him to his wondrous discovery of fractals. His work lives on today in many innovations in science, design, telecommunications, medicine, renewable energy, film (special effects), gaming (computer graphics) and more."


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.

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
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:

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.



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!