Computational Legal Studies™ » complex systems

Model Thinking – A Free Online Course with Scott E. Page (Director of UMich Center for Study of Complex Systems)

Daniel Martin Katz — Thu, 01 Dec 2011 18:32:05 +0000

Starting in the January 2012, Scott E. Page (one of my PhD thesis advisors) will teach Model Thinking (a free online course offered via the consortium that brought you AI Class, Machine Learning, etc.)

Scott and I have previously teamed up to teach Complex Systems @ the ICPSR Summer Methods Program (where I teach the model implementation lab). Over 7,000 people and counting have are already signed up …

Introduction to Computing for Complex Systems – Slides and Other Course Materials from ICPSR 2011

Daniel Martin Katz — Fri, 18 Nov 2011 14:15:47 +0000

I am going to bump this post to front of the blog one last time as there has been some interest in this material. It has now been several weeks since we completed the full four week class here at the ICPSR Program in Quantitative Methods. In this course, I (together with my colleagues) highlight the methods of complex systems as well as several environments designed to explore the field. These include Netlogo (agent based models and network models), Nova (system dynamics / ecological modeling) and Pajek (empirical network analysis). In addition, we a variety of advanced topics including:

(a) Community Detection in Networks
(b) Computational Linguistics / Natural Language Processing
(c) Diffusion Models and Mathematical Modeling with Data
(d) Exponential Random Graph (p*) Models
(e) Big Data/ Information Retrieval / Webscraping

Although, we do not work with more advanced languages within the course, those who need to conduct complex analysis are directed to alternatives such as R, Python, Java, etc.

Anyway, the slides are designed to be fully self-contained and thus allow for individually paced study of the relevant material. If you work through the slides carefully you should be able to learn the software as well as many of the core principles associated with the science of complex systems. The material should be available online indefinitely. If you have questions, feel free to email me.

Allan Jones: A Map of the Brain (The Same Way We Understand a City?) [ Ted 2011 ]

Daniel Martin Katz — Wed, 16 Nov 2011 02:50:01 +0000

Physicist Cuts Plane Boarding Time in Half [via CNet & Hacker News]

Daniel Martin Katz — Wed, 31 Aug 2011 04:06:29 +0000

“… Steffen considered various methods, such as boarding people in blocks, at random, and in window seats first. He set up a model using an algorithm based on the Monte Carlo optimization method used in statistics and mathematics. He found that the most efficient boarding method is to board alternate rows at a time, beginning with the window seats on one side, then the other, minimizing aisle interference. The window seats are followed by alternate rows of middle seats, then aisle seats. He also found that boarding at random is faster that boarding by blocks.” Coverage over at C-Net

Mark Pagel: How Language Transformed Humanity [ TED 2011 ]

Daniel Martin Katz — Sat, 27 Aug 2011 05:09:45 +0000

Managing Complex Organizations [ via HBR ]

Daniel Martin Katz — Tue, 23 Aug 2011 15:37:37 +0000

Geoffrey West : The Surprising Math of Cities and Corporations [ Ted Global 2011 ]

Daniel Martin Katz — Fri, 05 Aug 2011 05:50:16 +0000

Tim Harford: Trial, Error and the God Complex [ Ted 2011 ]

Daniel Martin Katz — Tue, 19 Jul 2011 20:55:39 +0000

Economics writer Tim Harford studies complex systems — and finds a surprising link among the successful ones: they were built through trial and error. In this sparkling talk from TEDGlobal 2011, he asks us to embrace our randomness and start making better mistakes.

Introduction to Computing for Complex Systems – Slides and Other Course Materials from ICPSR 2010

Daniel Martin Katz — Tue, 28 Jun 2011 22:51:50 +0000

I decided to bump this post to front of the blog as I am getting ready to dust off this material in anticipation of the 2011 ICPSR Course in Complex Systems Models in the Social Sciences. The course will be offered as part of Session #2 of the ICPSR Program in Quantitative Methods. It has two components (1) morning lectures on complex systems theory and (2) a late afternoon session on complex systems / computational model implementation.

Unlike many courses that relegate model/data implementation to self-teaching, etc. in the ICPSR Summer Course in Complex Systems, we take implementation seriously. Indeed, I believe our emphasis on implementation is a distinguishing feature of the course. In my experience, implementation mechanics are typically the impediment that many scholars face in generating models capable of being published in academic journals. Implementation is the bridge between concept and scientific contribution.

In the computing module, I (together with my colleagues) highlight the methods of complex systems as well as several environments designed to explore this rich and growing intellectual field. These include Netlogo (agent based models and network models), Vensim (system dynamics / ecological modeling) and Pajek (empirical network analysis). In the final week, we cover a variety of advanced topics:

(a) Community Detection in Networks
(b) Computational Linguistics / Natural Language Processing
(c) Diffusion Models and Mathematical Modeling with Data
(d) Exponential Random Graph (p*) Models
(e) Information Retrieval / Webscraping

Although, we do not work with more advanced languages within the course, those who need to conduct complex analysis are directed to alternatives such as R, Python, Java, etc.

Anyway, the slides were designed to be fully self-contained and thus allow for individually paced study of the relevant material. If you work through the slides carefully you should be able to learn the software as well as many of the core principles associated with the science of complex systems. Although the 2010 course material should be available indefinitely, I do plan to add some new material for the 2011 session. In particular, we plan to highlight Nova — a new software package developed in the Oberlin Computer Science Department by Richard Salter. Stay tuned for more in July / August 2011 ….

The Hindsight Fallacy: The Real Reason It’s So Hard to Predict Bubbles [Via Slate]

Daniel Martin Katz — Sat, 25 Jun 2011 17:01:26 +0000

Chris Burden: Metropolis II [An Artist's Perspective on Traffic Dynamics and Complex Adaptive Systems]

Daniel Martin Katz — Thu, 23 Jun 2011 04:48:20 +0000

Controllability of Complex Networks [via Nature]

Daniel Martin Katz — Wed, 18 May 2011 16:58:57 +0000

Abstract: “The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.”

Dynamic Reconfiguration of Human Brain Networks during Learning [From PNAS]

Daniel Martin Katz — Wed, 11 May 2011 17:18:51 +0000

Abstract: “Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.”