Archive for the ‘Complexity’ Category

The Art & Science Factory

Monday, January 15th, 2018

The Art & Science Factory

From the about page:

The Art & Science Factory was started in 2008 by Dr. Brian Castellani to organize the various artistic, scientific and educational endeavours he and different collaborators have engaged in to address the growing complexity of global life.

Dr. Castellani is a complexity scientist/artist.

He is internationally recognized for his expertise in complexity science and its history and for his development of the SACS Toolkit, a case-based, mixed-methods, computationally-grounded framework for modeling complex systems. Dr. Castellani’s main area of study is applying complexity science and the SACS Toolkit to various topics in health and healthcare, including community health and medical education.

In terms of visual complexity, Castellani is recognized around the world for his creation of the complexity map, which can be found on Wikipedia and on this website. He is also recognized for his blog on “all things complexity science and art,” the Sociology and Complexity Science Blog.
… (emphasis in original)

Dr. Castellani apparently dislikes searchable text, the about page quote being hand transcribed from an image that is that page.

Unexpectedly, the SACS toolkit, etc. were not hyperlinks so: SACS toolkit, complexity map, and Sociology and Complexity Science Blog, respectively.

2018 Map of the Complexity Sciences

Monday, January 15th, 2018

2018 Map of the Complexity Sciences by Brian Castellani.

At full screen this map barely displays on my 22″ monitor so I’m not going to mangle it into something smaller for this post.

The reading instructions read in part:

Also, in order to present some type of organizational structure, the history of the complexity sciences is developed along the field’s five major intellectual traditions: dynamical systems theory (purple), systems science (blue, complex systems theory (yellow, cybernetics (gray) and artificial intelligence (orange. Again, the fit is not exact (and sometimes even somewhat forced); but it is sufficient to help those new to the field gain a sense of its evolving history.

The subject and person nodes are all hyperlinks to additional resources!


Is It Foolish To Model Nature’s Complexity With Equations?

Thursday, October 29th, 2015

Is It Foolish To Model Nature’s Complexity With Equations? by Gabriel Popkin.

From the post:

Sometimes ecological data just don’t make sense. The sockeye salmon that spawn in British Columbia’s Fraser River offer a prime example. Scientists have tracked the fishery there since 1948, through numerous upswings and downswings. At first, population numbers seemed inversely correlated with ocean temperatures: The northern Pacific Ocean surface warms and then cools again every few decades, and in the early years of tracking, fish numbers seemed to rise when sea surface temperature fell. To biologists this seemed reasonable, since salmon thrive in cold waters. Represented as an equation, the population-temperature relationship also gave fishery managers a basis for setting catch limits so the salmon population did not crash.

But in the mid-1970s something strange happened: Ocean temperatures and fish numbers went out of sync. The tight correlation that scientists thought they had found between the two variables now seemed illusory, and the salmon population appeared to fluctuate randomly.

Trying to manage a major fishery with such a primitive understanding of its biology seems like folly to George Sugihara, an ecologist at the Scripps Institution of Oceanography in San Diego. But he and his colleagues now think they have solved the mystery of the Fraser River salmon. Their crucial insight? Throw out the equations.

Sugihara’s team has developed an approach based on chaos theory that they call “empirical dynamic modeling,” which makes no assumptions about salmon biology and uses only raw data as input. In designing it, the scientists found that sea surface temperature can in fact help predict population fluctuations, even though the two are not correlated in a simple way. Empirical dynamic modeling, Sugihara said, can reveal hidden causal relationships that lurk in the complex systems that abound in nature.

Sugihara and others are now starting to apply his methods not just in ecology but in finance, neuroscience and even genetics. These fields all involve complex, constantly changing phenomena that are difficult or impossible to predict using the equation-based models that have dominated science for the past 300 years. For such systems, DeAngelis said, empirical dynamic modeling “may very well be the future.”

If you like success stories with threads of chaos, strange attractors, and fractals running through them, you will enjoy Gabriel’s account of empirical dynamic modeling.

I have been a fan of chaos and fractals since reading Computer Recreations: A computer microscope zooms in for a look at the most complex object in mathematics in 1985 (Scientific American). That article was reposted as part of: DIY Fractals: Exploring the Mandelbrot Set on a Personal Computer by A. K. Dewdney.

Despite that long association with and appreciation of chaos theory, I would answer the title question with a firm maybe.

The answer depends upon whether equations or empirical dynamic modeling provide the amount of precision needed for some articulated purpose.

Both methods ignore any number of dimensions of data, each of which are as chaotic as any of the others. Which ones are taken into account and which ones are ignored is a design question.

Recitation of the uncertainty of data and analysis would be boring as a preface to every publication, but those factors should be upper most in the minds of every editor or reviewer.

Our choice of data or equations or some combination of both to simplify the world for reporting to others shapes the view we report.

What is foolish is to confuse those views with the world. They are not the same.

Updates from Complexity Explorer

Wednesday, October 7th, 2015

Updates from Complexity Explorer

Among other news:

Sid Redner on his Introduction to Random Walks tutorial [interview]

From the post:

Three tutorials coming soon: We are in the process of developing three new tutorials for you. Matrix and Vector Algebra, Information Theory, and Computation Theory. Stay tuned! And in the meantime, have you taken our latest tutorials, Maximum Entropy Methods and Random Walks?

Current courses: Fractals and Scaling and Nonlinear Dynamics are happening now! You can still join in these two fantastic courses if you haven’t already. Fractals and Scaling will end October 23rd, and Nonlinear Dynamics is set to end December 1st.

Agent-based Modeling: The Agent-based Modeling course has been delayed and will now be launched in 2016. We will let you know as soon as we have a clearer idea of the timeframe. You just can’t rush a good thing!

If you haven’t visited Complexity Explorer recently then it is time to catch up.

It is clear than none of the likely candidates for U.S. President in 2016 have ever heard of complexity! At least to judge from their overly simple and deterministic claims and policies.

Avoid their mistake, take a tutorial or course at the Complexity Explorer soon!

Modeling and Analysis of Complex Systems

Saturday, August 15th, 2015

Introduction to the Modeling and Analysis of Complex Systems by Hiroki Sayama.

From the webpage:

Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

This textbook is available for purchase in both grayscale and color via and

Do us all a favor and pass along the purchase options for classroom hard copies. This style of publishing will last only so long as a majority of us support it. Thanks!

From the introduction:

This is an introductory textbook about the concepts and techniques of mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of complex systems science. Complex systems can be informally defined as networks of many interacting components that may arise and evolve through self-organization. Many real-world systems can be modeled and understood as complex systems, such as political organizations, human cultures/languages, national and international economies, stock markets, the Internet, social networks, the global climate, food webs, brains, physiological systems, and even gene regulatory networks within a single cell; essentially, they are everywhere. In all of these systems, a massive amount of microscopic components are interacting with each other in nontrivial ways, where important information resides in the relationships between the parts and not necessarily within the parts themselves. It is therefore imperative to model and analyze how such interactions form and operate in order to understand what will emerge at a macroscopic scale in the system.

Complex systems science has gained an increasing amount of attention from both inside and outside of academia over the last few decades. There are many excellent books already published, which can introduce you to the big ideas and key take-home messages about complex systems. In the meantime, one persistent challenge I have been having in teaching complex systems over the last several years is the apparent lack of accessible, easy-to-follow, introductory-level technical textbooks. What I mean by technical textbooks are the ones that get down to the “wet and dirty” details of how to build mathematical or
computational models of complex systems and how to simulate and analyze them. Other books that go into such levels of detail are typically written for advanced students who are already doing some kind of research in physics, mathematics, or computer science. What I needed, instead, was a technical textbook that would be more appropriate for a broader audience—college freshmen and sophomores in any science, technology, engineering, and mathematics (STEM) areas, undergraduate/graduate students in other majors, such as the social sciences, management/organizational sciences, health sciences and the humanities, and even advanced high school students looking for research projects who are interested in complex systems modeling.

Can you imagine that? A technical textbook appropriate for a broad audience?

Perish the thought!

I could name several W3C standards that could have used that editorial stance as opposed to: “…we know what we meant….”

I should consider that as a market opportunity, to translate insider jargon (and deliberately so) into more generally accessible language. Might even help with uptake of the standards.

While I think about that, enjoy this introduction to complex systems, with Python none the less.

Complexity Updates!

Thursday, March 12th, 2015

The Complexity Explorer (Santa Fe Institute) has posted several updates to its homepage.

No news courses for Spring 2015. The break will be spent working on new mathematics modules, Vector and Matrix Algebra and Maximum Entropy Methods, due out later this year. Previous Santa Fe Complexity Courses are online.

If you need a complexity “fix” pushed at you, try the Twitter or Facebook.

If you are more than a passive consumer of news, volunteers are needed for:

Subtitling videos (something was said about a T-shirt, check the site for details), and

Other volunteer opportunities.


Big Data, or Not Big Data: What is <your> question?

Monday, February 16th, 2015

Big Data, or Not Big Data: What is <your> question? by Pradyumna S. Upadrashta.

From the post:

Before jumping on the Big Data bandwagon, I think it is important to ask the question of whether the problem you have requires much data. That is, I think its important to determine when Big Data is relevant to the problem at hand.

The question of relevancy is important, for two reasons: (i) if the data are irrelevant, you can’t draw appropriate conclusions (collecting more of the wrong data leads absolutely nowhere), (ii) the mismatch between the problem statement, the underlying process of interest, and the data in question is critical to understand if you are going to distill any great truths from your data.

Big Data is relevant when you see some evidence of a non-linear or non-stationary generative process that varies with time (or at least, collection time), on the spectrum of random drift to full blown chaotic behavior. Non-stationary behaviors can arise from complex (often ‘hidden’) interactions within the underlying process generating your observable data. If you observe non-linear relationships, with underlying stationarity, it reduces to a sampling problem. Big Data implicitly becomes relevant when we are dealing with processes embedded in a high dimensional context (i.e., after dimension reduction). For high embedding dimensions, we need more and more well distributed samples to understand the underlying process. For problems where the underlying process is both linear and stationary, we don’t necessarily need much data


Great post and a graphic that is worthy of being turned into a poster! (Pradyumna asks for suggestions on the graphic so you may want to wait a few days to see if it improves. Plus send suggestions if you have them.)

What is <your> question? wasn’t the starting point for: Dell: Big opportunities missed as Big Data remains big business.

The barriers to big data:

While big data has proven marketing benefits, infrastructure costs (35 per cent) and security (35 per cent) tend to be the primary obstacles for implementing big data initiatives.

Delving deeper, respondents believe analytics/operational costs (34 per cent), lack of management support (22 per cent) and lack of technical skills (21 per cent) are additional barriers in big data strategies.

“So where do the troubles with big data stem from?” asks Jones, citing cost (e.g. price of talent, storage, etc.), security concerns, uncertainty in how to leverage data and a lack of in-house expertise.

“In fact, only 36 percent of organisations globally have in-house big data expertise. Yet, the proven benefits of big data analytics should justify the investment – businesses just have to get started.

Do you see What is <your> question? being answered anywhere?

I didn’t, yet the drum beat for big data continues.

I fully agree that big data techniques and big data are important advances and they should be widely adopted and used, but only when they are appropriate to the question at hand.

Otherwise you will be like a non-profit I know that spent upward of $500,000+ on a CMS system that was fundamentally incompatible with their data. Wasn’t designed for document management. Fine system but not appropriate for the task at hand. It was like a sleeping dog in the middle of the office. No matter what you wanted to do, it was hard to avoid the dog.

Certainly could not admit that the purchasing decision was a mistake because those in charge would lose face.

Don’t find yourself in a similar situation with big data.

Unless and until someone produces an intelligible business plan that identifies the data, the proposed analysis of the data and the benefits of the results, along with cost estimates, etc., keep a big distance from big data. Make business ROI based decisions, not cult ones.

I first saw this in a tweet by Kirk Borne.

On the Computational Complexity of MapReduce

Monday, October 27th, 2014

On the Computational Complexity of MapReduce by Jeremy Kun.

From the post:

I recently wrapped up a fun paper with my coauthors Ben Fish, Adam Lelkes, Lev Reyzin, and Gyorgy Turan in which we analyzed the computational complexity of a model of the popular MapReduce framework. Check out the preprint on the arXiv.

As usual I’ll give a less formal discussion of the research here, and because the paper is a bit more technically involved than my previous work I’ll be omitting some of the more pedantic details. Our project started after Ben Moseley gave an excellent talk at UI Chicago. He presented a theoretical model of MapReduce introduced by Howard Karloff et al. in 2010, and discussed his own results on solving graph problems in this model, such as graph connectivity. You can read Karloff’s original paper here, but we’ll outline his model below.

Basically, the vast majority of the work on MapReduce has been algorithmic. What I mean by that is researchers have been finding more and cleverer algorithms to solve problems in MapReduce. They have covered a huge amount of work, implementing machine learning algorithms, algorithms for graph problems, and many others. In Moseley’s talk, he posed a question that caught our eye:

Is there a constant-round MapReduce algorithm which determines whether a graph is connected?

After we describe the model below it’ll be clear what we mean by “solve” and what we mean by “constant-round,” but the conjecture is that this is impossible, particularly for the case of sparse graphs. We know we can solve it in a logarithmic number of rounds, but anything better is open.

In any case, we started thinking about this problem and didn’t make much progress. To the best of my knowledge it’s still wide open. But along the way we got into a whole nest of more general questions about the power of MapReduce. Specifically, Karloff proved a theorem relating MapReduce to a very particular class of circuits. What I mean is he proved a theorem that says “anything that can be solved in MapReduce with so many rounds and so much space can be solved by circuits that are yae big and yae complicated, and vice versa.

But this question is so specific! We wanted to know: is MapReduce as powerful as polynomial time, our classical notion of efficiency (does it equal P)? Can it capture all computations requiring logarithmic space (does it contain L)? MapReduce seems to be somewhere in between, but it’s exact relationship to these classes is unknown. And as we’ll see in a moment the theoretical model uses a novel communication model, and processors that never get to see the entire input. So this led us to a host of natural complexity questions:

  1. What computations are possible in a model of parallel computation where no processor has enough space to store even one thousandth of the input?
  2. What computations are possible in a model of parallel computation where processor’s can’t request or send specific information from/to other processors?
  3. How the hell do you prove that something can’t be done under constraints of this kind?
  4. How do you measure the increase of power provided by giving MapReduce additional rounds or additional time?

These questions are in the domain of complexity theory, and so it makes sense to try to apply the standard tools of complexity theory to answer them. Our paper does this, laying some brick for future efforts to study MapReduce from a complexity perspective.

Given the prevalence of MapReduce, progress on understanding what is or is not possible is an important topic.

The first two complexity questions strike me as the ones most relevant to topic map processing with map reduce. Depending upon the nature of your merging algorithm.


Know Thy Complexities!

Saturday, January 4th, 2014

Know Thy Complexities!

From the post:

Hi there! This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science. When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn’t be stumped when asked about them. Over the last few years, I’ve interviewed at several Silicon Valley startups, and also some bigger companies, like Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to myself “Why oh why hasn’t someone created a nice Big-O cheat sheet?”. So, to save all of you fine folks a ton of time, I went ahead and created one. Enjoy!

The algorithms are linked to appropriate entries in Wikipedia.

But other data exists on these algorithms and new results are common.

If this is a “cheatsheet” view, what other views of that data would you create?

I first saw this in a tweet by The O.C.R.

The Scourge of Unnecessary Complexity

Thursday, December 19th, 2013

The Scourge of Unnecessary Complexity by Stephen Few.

From the post:

One of the mottos of my work is “eloquence through simplicity:” eloquence of communication through simplicity of design. Simple should not be confused with simplistic (overly simplified). Simplicity’s goal is to find the simplest way to represent something, stripping away all that isn’t essential and expressing what’s left in the clearest possible way. It is the happy medium between too much and too little.

While I professionally strive for simplicity in data visualization, I care about it in all aspects of life. Our world is overly complicated by unnecessary and poorly expressed information and choices, and the problem is getting worse in our so-called age of Big Data. Throughout history great thinkers have campaigned for simplicity. Steve Jobs was fond of quoting Leonardo da Vinci: “Simplicity is the ultimate sophistication.” Never has the need for such a campaign been greater than today.

A new book, Simple: Conquering the Crisis of Complexity, by Alan Siegal and Irene Etzkorn, lives up to its title by providing a simple overview of the need for simplicity, examples of simplifications that have already enriched our lives (e.g., the 1040EZ single-page tax form that the authors worked with the IRS to design), and suggestions for what we can all do to simplify the world. This is a wonderful book, filled with information that’s desperately needed.

Too late for Christmas but I have a birthday coming up. 😉

Sounds like a great read and a lesson to be repeated often.

Complex documentation and standards only increase the cost of using software or implementing standards.

Whose interest is advanced by that?

Design Fractal Art…

Monday, October 21st, 2013

Design Fractal Art on the Supercomputer in Your Pocket


From the post:

Fractals are deeply weird: They’re mathematical objects whose infinite “self-similarity” means that you can zoom into them forever and keep seeing the same features over and over again. Famous fractal patterns like the Mandelbrot set tend to get glossed over by the general public as neato screensavers and not much else, but now a new iOS app called Frax is attempting to bridge that gap.

Frax, to its credit, leans right into the “ooh, neat colors!” aspect of fractal math. The twist is that the formidable processing horsepower in current iPhones and iPads allows Frax to display and manipulate these visual patterns in dizzying detail–far beyond the superficial treatment of, say, a screensaver. “The iPhone was the first mobile device to have the horsepower to do realtime graphics like this, so we saw the opportunity to bring the visual excitement of fractals to a new medium, and in a new style,” says Ben Weiss, who created Frax with UI guru Kai Krause and Tom Beddard (a designer we’ve written about before). “As the hardware has improved, the complexity of the app has grown exponentially, as has its performance.” Frax lets you pan, zoom, and animate fractal art–plus play with elaborate 3-D and lighting effects.

I was afraid of this day.

The day when I would see an iPhone or iPad app that I just could not live without. 😉

If you think fractals are just pretty, remember Fractal Tree Indexing? And TukoDB?

From later in the post:

Frax offers a paid upgrade which unlocks hundreds of visual parameters to play with, as well as access to Frax’s own cloud-based render farm (for outputting your mathematical masterpieces at 50-megapixel resolution).

The top image in this post is also from the original post.

I first saw this in a tweet by IBMResearch.

Measuring the Complexity of the Law: The United States Code

Wednesday, August 21st, 2013

Measuring the Complexity of the Law: The United States Code by Daniel Martin Katz and Michael James Bommarito II.


Einstein’s razor, a corollary of Ockham’s razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face — to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity’s inverse, taxes cognition and increases the likelihood of suboptimal decisions. In addition, unnecessary legal complexity can drive a misallocation of human capital toward comprehending and complying with legal rules and away from other productive ends.

While many scholars have offered descriptive accounts or theoretical models of legal complexity, empirical research to date has been limited to simple measures of size, such as the number of pages in a bill. No extant research rigorously applies a meaningful model to real data. As a consequence, we have no reliable means to determine whether a new bill, regulation, order, or precedent substantially effects legal complexity.

In this paper, we address this need by developing a proposed empirical framework for measuring relative legal complexity. This framework is based on “knowledge acquisition,” an approach at the intersection of psychology and computer science, which can take into account the structure, language, and interdependence of law. We then demonstrate the descriptive value of this framework by applying it to the U.S. Code’s Titles, scoring and ranking them by their relative complexity. Our framework is flexible, intuitive, and transparent, and we offer this approach as a first step in developing a practical methodology for assessing legal complexity.

Curious what you make of the treatment of the complexity of language of laws in this article?

The authors compute the number of words and the average length of words in each title of the United States Code. In addition, the Shannon entropy of each title is also calculated. Those results figure in the author’s determination of the complexity of each title.

To be sure, those are all measurable aspects of each title and so in that sense the results and the process to reach them can be duplicated and verified by others.

The author’s are using a “knowledge acquisition model,” that is measuring the difficulty a reader would experience in reading and acquiring knowledge about any part of the United States Code.

But reading the bare words of the U.S. Code is not a reliable way to acquire legal knowledge. Words in the U.S. Code and their meanings have been debated and decided (sometimes differently) by various courts. A reader doesn’t understand the U.S. Code without knowledge of court decisions on the language of the text.

Let me give you a short example:

42 U.S.C. §1983 read:

Every person who, under color of any statute, ordinance, regulation, custom, or usage, of any State or Territory or the District of Columbia, subjects, or causes to be subjected, any citizen of the United States or other person within the jurisdiction thereof to the deprivation of any rights, privileges, or immunities secured by the Constitution and laws, shall be liable to the party injured in an action at law, suit in equity, or other proper proceeding for redress, except that in any action brought against a judicial officer for an act or omission taken in such officer’s judicial capacity, injunctive relief shall not be granted unless a declaratory decree was violated or declaratory relief was unavailable. For the purposes of this section, any Act of Congress applicable exclusively to the District of Columbia shall be considered to be a statute of the District of Columbia. (emphasis added)

Before reading the rest of this post, answer this question: Is a municipality a person for purposes of 42 U.S.C. §1983?

That is if city employees violate your civil rights, can you sue them and the city they work for?

That seems like a straightforward question. Yes?

In Monroe v. Pape, 365 US 167 (1961), the Supreme Court found the answer was no. Municipalities were not “persons” for purposes of 42 U.S.C. §1983.

But a reader who only remembers that decision would be wrong if trying to understand that statute today.

In Monell v. New York City Dept. of Social Services, 436 U.S. 658 (1978), the Supreme Court found that it was mistaken in Monroe v. Pape and found the answer was yes. Municipalities could be “persons” for purposes of 42 U.S.C. §1983, in some cases.

The language in 42 U.S.C. §1983 did not change between 1961 and 1978. Nor did the circumstances under which section 1983 was passed (Civil War reconstruction) change.

But the meaning of that one word changed significantly.

Many other words in the U.S. Code have had a similar experience.

If you need assistance with 42 U.S.C. §1983 or any other part of the U.S. Code or other laws, seek legal counsel.

Complex Adaptive Dynamical Systems, a Primer

Friday, August 9th, 2013

Complex Adaptive Dynamical Systems, a Primer by Claudius Gros. (PDF)

The high level table of contents should capture your interest:

  1. Graph Theory and Small-World Networks
  2. Chaos, Bifurcations and Diffusion
  3. Complexity and Information Theory
  4. Random Boolean Networks
  5. Cellular Automata and Self-Organized Criticality
  6. Darwinian Evolution, Hypercycles and Game Theory
  7. Synchronization Phenomena
  8. Elements of Cognitive Systems Theory

If not, you can always try the video lectures by the author.

While big data is a crude approximation of some part of the world as we experience it, it is less coarse than prior representations.

Curious how less coarse representations will need to become in order to exhibit the complex behavior of what they represent?

I first saw this at Complex Adaptive Dynamical Systems, a Primer (Claudius Gros) by Charles Iliya Krempeaux.

Introduction to Complexity course is now enrolling!

Tuesday, February 5th, 2013

Santa Fe Institute’s Introduction to Complexity course is now enrolling!

From the webpage:

This free online course is open to anyone, and has no prerequisites. Watch the Intro Video to learn what this course is about and how to take it. Enroll to sign up, and you can start the course immediately. See the Syllabus and the Frequently Asked Questions to learn more.

I am waiting for the confirmation email now.

Definitely worth your attention.

Not that I think subject identity is fractal in nature.

Fractals as you know have a self-similarity property and at least in my view, subject identity does not.

As you explore a subject identity, you encounter other subjects identities, which isn’t the same thing as being self-similar.

Or should I say you will encounter complexities of subject identities?

Like all social constructs, identification of a subject is simple because we have chosen to view it that way.

Are you ready to look beyond our usual assumptions?

Kolmogorov Complexity – A Primer

Thursday, December 6th, 2012

Kolmogorov Complexity – A Primer by Jeremy Kun.

From the post:

Previously on this blog (quite a while ago), we’ve investigated some simple ideas of using randomness in artistic design (psychedelic art, and earlier randomized css designs), and measuring the complexity of such constructions. Here we intend to give a more thorough and rigorous introduction to the study of the complexity of strings. This naturally falls into the realm of computability theory and complexity theory, and so we refer the novice reader to our other primers on the subject (Determinism and Finite Automata, Turing Machines, and Complexity Classes; but Turing machines will be the most critical to this discussion).

Jeremy sets the groundwork necessary for a later post in this series. (covering machine learning)

Digest this for a couple of days and I will point out the second post.

Update: Introduction to Complexity [Santa Fe Institute]

Wednesday, December 5th, 2012

The Santa Fe Institute has released the FAQ and syllabus for its “Introduction to Complexity” course in 2013.

The course starts January 28, 2013 and will last for eleven (11) weeks.

Lecture units:

  1. What is Complexity?
  2. Dynamics, Chaos, and Fractals
  3. Information, Order, and Randomness
  4. Cellular Automata
  5. Genetic Algorithms
  6. Self-Organization in Nature
  7. Modeling Social Systems
  8. Networks
  9. Scaling
  10. Cities as Complex Systems
  11. Course Field Trip; Final Exam

Funding permitting there may be a Complexity part II in the summer of 2013.

Your interest and participation in this course may help drive the appearance of the second course.

An earlier post on the course: Introduction to Complexity [Santa Fe Institute].

Complexity Explorer Project

Saturday, November 3rd, 2012

Complexity Explorer Project

A website development project that reports that when “live” it will serve (among others):

Scientist keeping up to date on papers with Source Materials Search Engine and Paper Summaries

Professor designing new course on complexity

High-school science teacher using virtual laboratory for student science projects

Non-expert learning how complex systems science relates to their own field

Scheduled to go beta in the Fall of 2012.

As always, of interest to see how semantic issues are handled in research/library settings.

Introduction to Complexity [Santa Fe Institute]

Saturday, November 3rd, 2012

Introduction to Complexity [Santa Fe Institute]

From the webpage:

Santa Fe Institute will be launching a series of MOOCs (Massive Open On-line Courses), covering the field of complex systems science. Our first course, Introduction to Complexity, will be an accessible introduction to the field, with no pre-requisites. You don’t need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.

In this ten-week course, you’ll learn about the tools used by complex systems scientists to understand, and sometimes to control, complex systems. The topics you’ll learn about include dynamics, chaos, fractals, information theory, computation theory, evolution and adaptation, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology.

Introduction to Complexity will be free and open to anyone. The instructor is Melanie Mitchell, External Professor at SFI, Professor of Computer Science at Portland State University, and author of the award-winning book, Complexity: A Guided Tour. The course will begin in early 2013.

You can subscribe to course announcements at this page.

If you don’t know the Santa Fe Institute, you should.

Dreams of Universality, Reality of Interdisciplinarity [Indexing/Mapping Pidgin]

Tuesday, June 12th, 2012

Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity by Sebastian Grauwin, Guillaume Beslon, Eric Fleury, Sara Franceschelli, Jean-Baptiste Rouquier, and Pablo Jensen.


Using a large database (~ 215 000 records) of relevant articles, we empirically study the “complex systems” field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific “trading zones”, ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network).

If disciplines don’t understand each other…:

Where do the links come from then? In an illuminating analogy, Peter Galison [32] compares the difficulty of connecting scientifi c disciplines to the difficulty of communicating between diff erent languages. History of language has shown that when two cultures are strongly motivated to communicate – generally for commercial reasons – they develop simpli ed languages that allow for simple forms of interaction. At first, a “foreigner talk” develops, which becomes a “pidgin” when social uses consolidate this language. In rare cases, the “trading zone” stabilizes and the expanded pidgin becomes a creole, initiating the development of an original, autonomous culture. Analogously, biologists may create a simpli ed and partial version of their discipline for interested physicists, which may develop to a full-blown new discipline such as biophysics. Specifi cally, Galison has studied [32] how Monte Carlo simulations developed in the postwar period as a trading language between theorists, experimentalists, instrument makers, chemists and mechanical engineers. Our interest in the concept of a trading zone is to allow us to explore the dynamics of the interdisciplinary interaction instead of ending analysis by reference to a “symbiosis” or “collaboration”.

My interest is in how to leverage “trading zones” for the purpose of indexing and mapping (as in topic maps).

Noting that “trading zones” are subject to emprical discovery and no doubt change over time.

Discovering and capitalizing on such “trading zones” will be a real value-add for users.

Lima on Networks

Thursday, May 24th, 2012

I saw a mention of RSA Animate – The Power of Networks by Manuel Lima over at Flowing Data.

A high speed chase through ideas but the artistry of the presentation and presenter make it hold together quite nicely.

Manuel makes the case that organization of information is more complex than trees. In fact, makes a good case for networks being a better model.

If that bothers you, you might want to cut Manuel some slack or perhaps even support the “network” (singular) model.

There are those of us who don’t think a single network is sufficient.


Resources to review before viewing the video:

Science and Complexity – Warren Weaver (1948 – reprint): The paper that Manuel cites in his presentation.

Wikipedia – Complexity Not bad as Wikipedia entries go. At least a starting point.

Search and Exogenous Complexity – (inside vs. outside?)

Tuesday, January 31st, 2012

Search and Exogenous Complexity

Stephen Arnold writes:

I am now using the phrase “exogenous complexity” to describe systems, methods, processes, and procedures which are likely to fail due to outside factors. This initial post focuses on indexing, but I will extend the concept to other content centric applications in the future. Disagree with me? Use the comments section of this blog, please.

What is an outside factor?

Let’s think about value adding indexing, content enrichment, or metatagging. The idea is that unstructured text contains entities, facts, bound phrases, and other identifiable entities. A key word search system is mostly blind to the meaning of a number in the form nnn nn nnnn, which in the United States is the pattern for a Social Security Number. There are similar patterns in Federal Express, financial, and other types of sequences. The idea is that a system will recognize these strings and tag them appropriately; for example:

nnn nn nnn Social Security Number

Thus, a query for Social Security Numbers will return a string of digits matching the pattern. The same logic can be applied to certain entities and with the help of a knowledge base, Bayesian numerical recipes, and other techniques such as synonym expansion determine that a query for Obama residence will return White House or a query for the White House will return links to the Obama residence.

I am not sure the inside/outside division is helpful.

For example, Arnold starts with the issue:

First, there is the issue of humans who use language in unexpected or what some poets call “fresh” or “metaphoric” methods. English is synthetic in that any string of sounds can be used in quite unexpected ways.

True, but recall the overloading of owl:sameAs, which is entirely within a semantic system.

I mention that to make the point that while inside/outside may be useful informal metaphors, semantics are with us, always. Even in systems where one or more parties think the semantics are “obvious” or “defined.” Maybe, depends on who you ask.

The second issue is:

Second, there is the quite real problem of figuring out the meaning of short, mostly context free snippets of text.

But isn’t that an inside problem too? Search engines vacuum up content from a variety of contexts, not preserving the context that would make the “snippets of text” meaningful. Snippets of text have very different meanings in comp.compilers than in alt.religion.scientology. It is the searcher’s choice to treat both as a single pile of text.

His third point is:

Third, there is the issue of people and companies desperate for a solution or desperate for revenue. The coin has two sides. Individuals who are looking for a silver bullet find vendors who promise not just one silver bullet but an ammunition belt stuffed with the rounds. The buyers and vendors act out a digital kabuki.

But isn’t this an issue of design and requirements, which are “inside” issues as well?

No system can meet a requirement for universal semantic resolution with little or not human involvement. The questions are: How much better information do you need How much are you willing to pay for it? No free lunch when its comes to semantics, ever. That includes the semantics of the systems we use and the information to which they are applied.

The requirements for any search system must address both “inside” and “outside” issues and semantics.

(Apologies for the length but semantic complexity is one of my pet topics.)

Complexity and Computation

Thursday, January 12th, 2012

Complexity and Computation by Allen B. Downey.

Another free (you can order hard copy) book from Allen B. Downey. See my post: Think Stats: Probability and Statistics for Programmers or jump to Green Tea Press to see these and other titles for free download.


This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science:

  • Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower.

    An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph—nested dictionaries—and several graph algorithms that use this data structure.

  • Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python.
  • Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation.
  • Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology.

This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

Complexity science is an interdiscipinary field—at the intersection of mathematics, computer science and physics—that focuses on these kinds of models. That’s what this book is about.