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Complex Systems: A Survey

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From the abstract: “A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a short survey and an annotated bibliography of resources for those interested in learning about complex systems.” [By Mark E.J. Newman – Submitted to Amer. J. Physics]

Complex systems is a relatively young subject area and one that is evolving rapidly, but there are nonetheless a number of general references, including books and reviews, that bring together relevant topics in a useful way. ” The paper then has recommended materials on major topics relevant to the study of complex systems including:

  • Lattices and Networks
  • Dynamical Systems (including Chaos & Fractals)
  • Discrete Dynamics and Cellular Automata
  • Scaling and Criticality
  • Adaptation and Game Theory
  • Information Theory
  • Computational Complexity
  • Agent-Based Modeling

 

“Classic examples of complex systems include condensed matter systems, ecosystems, the economy and financial markets, the brain, the immune system, granular materials, road traffic, insect colonies, flocking or schooling behavior in birds or fish, the Internet, and even entire human societies.”

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Salman Khan: Let’s Use Video to Reinvent Education [ TED 2011 ]

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“In 2004, Salman Khan, a hedge fund analyst, began posting math tutorials on YouTube. Six years later, he has posted more than 2.000 tutorials, which are viewed nearly 100,000 times around the world. In this TED 2011 Talk,  Salman talks about how and why he created the remarkable Khan Academy, a carefully structured series of educational videos offering complete curricula in math and, now, other subjects. He shows the power of interactive exercises, and calls for teachers to consider flipping the traditional classroom script — give students video lectures to watch at home, and do “homework” in the classroom with the teacher available to help.”

This offers a pretty interesting alternative model for education delivery.  It is worth checking out!

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Deb Roy: The Birth of a Word [TED 2011]

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This is one of the better TED Talks I have seen to date.  It is definitely worth watching!  

From the abstract: MIT researcher Deb Roy wanted to understand how his infant son learned language — so he wired up his house with videocameras to catch every moment (with exceptions) of his son’s life, then parsed 90,000 hours of home video to watch “gaaaa” slowly turn into “water.” Astonishing, data-rich research with deep implications for how we learn.

For an interesting related talk, check out Patricia Kuhl– The linguistic genius of babies (TEDxRanier).

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How to Grow a Mind: Statistics, Structure, and Abstraction [via Science]

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From the abstract: “In coming to understand the world—in learning concepts, acquiring language, and grasping causal relations—our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?”

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Rock / Paper / Scissors – Man v. Machine (as t→∞ you are not likely to win) [via NY Times]

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From the site … “A truly random game of Rock-Paper-Scissors would result in a statistical tie with each player winning, tying and losing one-third of the time …  However, people are not truly random and thus can be studied and analyzed. While this computer won’t win all rounds, over time it can exploit a person’s tendencies and patterns to gain an advantage over its opponent.

Computers mimic human reasoning by building on simple rules and statistical averages. Test your strategy against the computer in this rock-paper-scissors game illustrating basic artificial intelligence. Choose from two different modes: novice, where the computer learns to play from scratch, and veteran, where the computer pits over 200,000 rounds of previous experience against you.”

Time to dust off your random seed / pseudorandom number generators … good luck!

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Some Papers We Have Been Reading [March 2011 Edition]

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Social Structure of Facebook Networks (By Amanda L. Traud, Peter J. Mucha & Mason A. Porter)

Practicing Theory: Legal Education for the Twenty-First Century (By Larry Ribstein)

How Judges Decide: A Multidimensional Empirical Typology of Judicial Styles in the Federal Courts (By Corey Rayburn Yung)

Brain Scans as Evidence: Truths, Proofs, Lies, and Lessons (By Francis X. Shen & Owen D. Jones)

Small but Slow World: How Network Topology and Burstiness Slow Down Spreading (By M. Karsai, M. Kivela, R. K. Pan, K. Kaski, J. Kertesz,  A.L. Barabasi & J. Saramaki)

Abandoning Law Reports for Official Digital Case Law (By Peter W. Martin)

Scaling of Prosocial Behavior in Cities (By Samuel Arbesman & Nicholas A. Christakis)

Empiricism and the Rising Incidence of Coauthorship in Law (By Tom Ginsburg & Tom Miles)

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Revising Zipf’s law [From PNAS]

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From the Abstract: “We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf’s 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.”

[ HT to Paul Kedrosky ]