Archive for the ‘Complexity’ Category

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.

Abstract:

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.

Description:

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.