Complex Systems in the Social & Physical Sciences – By Bestiario [Repost]
March 10th, 2010
Archive
Posts Tagged ‘complex systems’
Gregory Todd Jones — Evolution of Complexity and “Rethinking Individuality” at TedX Atlanta
March 9th, 2010
As a member of the Society for Evolutionary Analysis in Law (SEAL), I have had the oppurtunity to see a number of interesting presentations by Gregory Todd Jones. Gregory is a Faculty Research Fellow and Adjunct Professor of Law at the Georgia State University College of Law as well as Senior Director of Research and Principal Scientist at the Network for Collaborative Problem Solving. Of particular interest to readers of this blog, he is also the founding director of the Computational Laboratory for Complex Adaptive Systems at Georgia State Law School.
Above is a recent talk by Gregory at the TedX Atlanta in which he (1) assembles a model of sustainability based on collaboration and (2) discusses species behavior … from slugs to chimpanzees. If you are interested in learning more … Gregory has launched a really cool blog … Cooperation Science Blog … Check it out!
Complex Systems in the Social & Physical Sciences [By Bestiario]
December 17th, 2009
New Paper: Properties of the United States Code Citation Network
November 11th, 2009
We have been working on a larger paper applying many concepts from structural analysis and complexity science to the study of bodies of statutory law such as the United States Code. To preview the broader paper, we’ve published to SSRN and arXiv a shorter, more technical analysis of the properties of the United States Code’s network of citations.
Click here to Download the Paper!
Abstract: The United States Code is a body of documents that collectively comprises the statutory law of the United States. In this short paper, we investigate the properties of the network of citations contained within the Code, most notably its degree distribution. Acknowledging the text contained within each of the Code’s section nodes, we adjust our interpretation of the nodes to control for section length. Though we find a number of interesting properties in these degree distributions, the power law distribution is not an appropriate model for this system.
- Citation In-Degree
Science Magazine: Complex Systems & Networks [Repost from July 27]
August 14th, 2009
YouTube Research — Robust Dynamic Classes Revealed by Measuring the Response Function of a Social System
July 9th, 2009
Here at the CSCS Lab, we are working hard to finish up some projects. In the meantime, we wanted to highlight one of our favorite articles, an article we previously highlighted on the blog. Some of you might ask “what does this have to do with law or social science?” (1) We believe the taxonomy outlined in this article could potentially be applied to a wide set of social phenomena (2) As we say around here, if you are not reading outside your discipline, you are far less likely to be able to innovate within your discipline. So we suggest you consider downloading this paper….
The S.I.R. Model — A Simple Model With Applications to Swine Flu, etc.
April 28th, 2009
Last week we offered a model of intellectual diffusion built upon a standard fare social epidemiology model. Given recent events within the United States, Mexico and potentially worldwide, we thought it would be worthwhile to highlight the classic S.I.R. (Susceptible, Infected, Recovered) model. Netlogo offers a user friendly version of the model. Using this platform, we hope the exploration of the dynamics of S.I.R. might prove illuminating.
First, various hosts have different levels of interactions (work, home, transit, etc.) and so this network approach represents a blunt measure. To start the model at the default parameters, push the SETUP Button and then the GO Button. As the model runs, the plot tracks the Susceptible, Infected, Recovered. The model contains a variety of ”sliders.” The model can be rerun at lots of combinations of parameter levels. Those “sliders” fall into several categories: Network Attributes, Virus Attributes, Node Attributes. The full documentation is available here.
With respect to the swine flu, one important parameter is the delay between when an individual becomes infectious and when that individual is likely to become symptomatic. This parameter can be tuned in the simulation above using VIRUS-CHECK-FREQUENCY slider. From the documentation… “Infected nodes are not immediately aware that they are infected. Only every so often (determined by the VIRUS-CHECK-FREQUENCY slider) do the nodes check whether they are infected by a virus.”
An additional parameter worthy of consideration is the VIRUS-SPREAD-CHANCE. Consider this slider as a rough measure of the underlying infectiousness of the virus in question.
It is important to note the above simulation is an incredible simplification of the world faced by public health officials. Additionally, this version of the model was designed to consider the spread of disease on a computer network. Notwithstanding these limitations, we thought it useful to highlight a computational approach to this important matter of public concern.
