New paper: Modeling self-organizing traffic lights with elementary cellular automata

Carlos Gershenson and David A. Rosenblueth, "Modeling self-organizing traffic lights with elementary cellular automata", C3 Report No. 2009.06.

Abstract: There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions.

We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of "free-spaces" that flow in the opposite direction of traffic.

Full paper: http://arxiv.org/abs/0907.1925

Simulation available at: http://turing.iimas.unam.mx/~cgg/NetLogo/trafficCA.html

New Draft: What Does Artificial Life Tell Us About Death?

I just uploaded to the arXiv a first draft of a short essay "What Does Artificial Life Tell Us About Death?", you can download it at: http://arxiv.org/abs/0906.2824

Why Do Developing Countries Not Develop

After several years living abroad (St. Petersburg, Sussex, Brussels, Granada, Boston), it takes a bit of time to get used to life back in Mexico City.

Having some frame of comparison, I realized one way to describe the problem with developing countries such as Mexico: there is a high degree of incompetence.

Now, it is difficult to measure incompetence (In TeraBushes?), but to understand better what I mean, let us say that an agent (person, business, organization) is more incompetent if there are more tasks that the agent should perform successfully and it does not. I mean, I am quite incompetent in bureaucratic monotonous tedious labours, but these are not my duty (which some people refer to as "professional handwaving", others as "academia").

All countries and all agents have a certain degree of incompetence: nobody is perfect, there will always be errors, especially with novel tasks. Also, all countries and agents have a large degree of functionality, things that do work (somehow). However, in Mexico you run into incompetence across all scales more often than in other countries. Call it a higher error rate. To give an example, a couple of days ago I went to Home Depot to get some boards for some furniture I am making (another thing I am incompetent at...), and I was naïve enough not to check the boards at the shop. So, I start screwing^2 the boards together, and some of them aren't even square! Others have the wrong measures! I need to go back and return half the boards. OK, so Home Depot is a multinational company, and I suppose that you don't get this type of incompetence on all its locations. So, it is the error of the worker who chopped the boards.

Similar things happen every day: at restaurants you have a higher chance that the waiter will make an error with your order, our engineers have a higher chance of making an unusable bike track, our online banking systems have a higher chance of not working properly, our electricity has a higher chance of being interrupted, our presidents have a higher chance to make some silly agreement (and our congress of allowing it).

Where is the root of all this incompetence? I believe that we can say that in education. Countries which are less incompetent have much higher education rates. The average education in Mexico a few years back was five years per adult. This means that for each person with a PhD (~20 years of education), there are four with no education at all.

How can we improve education? This seems like a chicken and egg problem, because many teachers are quite incompetent (those who can, do; those who can't, teach...). And where do you get good teachers to teach teachers how to teach properly?

My opinion is that we need an alternative road to education, since it is too slow. It has improved, but it takes generations to make a difference. One option would be to increase awareness. If people are aware of their errors, they can try to correct them by themselves. If people are aware of the errors of others, they should complain (conformism and impunity are other big problems: errors are tolerated).

How to increase awareness? I would like to know, I need to increase mine. Meditation may help... I am sure that TV & church do not (we have >95% catholic population). Maybe a new online religion (read sect) combining scientific and oriental spiritual worldviews might do the trick...

Tentative Research Projects

I made a list of potential research projects I would like to explore with colleagues and/or students. You can find it here.

New Paper: Enfrentando a la Complejidad: Predecir vs. Adaptar

Resumen: Una de las presuposiciones de la ciencia desde los tiempos de Galileo, Newton y Laplace ha sido la previsibilidad del mundo. Esta idea ha influido en los modelos cientificos y tecnologicos. Sin embargo, en las ultimas decadas, el caos y la complejidad han mostrado que no todos los fenomenos son previsibles, aun siendo estos deterministas. Si el espacio de un problema es previsible, podemos en teoria encontrar una solucion por optimizacion. No obstante, si el espacio de un problema no es previsible, o cambia mas rapido de lo que podemos optimizarlo, la optimizacion probablemente nos dara una solucion obsoleta. Esto sucede con frecuencia cuando la solucion inmediata afecta el espacio del problema mismo. Una alternativa se encuentra en la adaptacion. Si dotamos a un sistema de esta propiedad, este mismo podra encontrar nuevas soluciones para situaciones no previstas.

Abstract: One of the assumptions of science since the times of Galileo, Newton, and Laplace has been the predictability of the world. This idea has influenced scientific and technological models. However, in the last decades, chaos and complexity have shown that not all phenomena are predictable, even if they are deterministic. If a problem space is predictable, we can in theory find a solution via optimization. Nevertheless, if a problem space is not predictable, or changes faster than we can optimize it, optimization probably will give us an obsolete solution. This often happens when the immediate solution affects the problem space itself. One alternative is found in adaptation. If we give this property to a system, the system will be able to find by itself new solutions for unforeseen situations.

Full article (in Spanish).

Webpage update

I finally was able to update my webpage. Its new location is
http://turing.iimas.unam.mx/~cgg/

WolframAlpha: The next big Web breakthrough?

When Google was released, it revolutionized the quality of search engines, transforming the world society: it enabled anyone with Internet access to find almost any information. Knowledge available to everyone.

The next big technology to have a global effect might be WolframAlpha, to be released in a few weeks. Its main goal is in line with the main vision of the Internet: to make expert-level knowledge accessible to anyone. It is complementary to search engines, which find data. WolframAlpha computes structured data.

I just listened to a preview webinar by Stephen Wolfram, the leader of the project where already several hundred people are working. In exploits the computational capabilities of Mathematica, huge databases and live feeds of structured data, an impressive free form linguistic analysis, and an automated presentation of relevant results.

It does much more than mathematics (from sums to integrals) It can calculate interesting comparisons, e.g. GDP of countries, weather of cities, popularity of names, stock and exchange rates, unit conversions, etc. It has data from physics, chemistry, life sciences, (human genome project included), engineering, astronomy (e.g. location of the ISS), computational sciences (NKS and other stuff), geographical and socioeconomical data, health and nutrition (e.g. nutritional information of food), linguistics (dictionary, find words with certain patterns), history, entertainment, sports, music, colors, etc. And that is just the beginning...

I asked Wolfram what are the big challenges ahead for WolframAlpha. It seems one of the biggest is people understanding what it really is about, what kind of things it can be useful for. I mean, it is quite impressive and it can do really a lot, but it is not an all-mighty oracle. Other challenges are the ability to use more than one line of input (as it is now), e.g. uploading spreadsheets, images, etc., and then let WolframAlpha to compute that data.

In principle, there is lots of expectation to the potential uses of this technology. We will see how the public welcomes it.

CfP: Swarm Cognition Workshop

Call for Papers: Swarm Cognition Workshop
http://laral.istc.cnr.it/swarm-cognition/Main_Page
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The workshop is part of the 31st Annual Meeting of the Cognitive Science Society (CogSci 2009 - http://cognitivesciencesociety.org/conference2009/ )

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Overview

Swarm Cognition" is the juxtaposition of two relatively unrelated concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the complexity of cognitive processes in the vertebrate brain. With this premise, the Swarm Cognition Workshop aims at promoting synergies between diverse disciplines such as cognitive neurosciences, psychology, ethology and swarm intelligence. Research work in Swarm Cognition aims at identifying the operational principles of cognitive behaviour by calling upon the underlying mechanisms of self-organising systems, i.e., systems whose internal organisation changes without being guided by an outside source.

For up to date information, see the workshop website:
http://laral.istc.cnr.it/swarm-cognition

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Important Dates

Paper submission deadline: 1 June 2009
Notice of acceptance: 12 June 2009
Camera ready deadline: 3 July 2009
Workshop date: 29 July 2009

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Format

We expect and encourage submissions about either work in progress or final results. Additionally, already published work recast into the Swarm Cognition framework are also eligible for presentation at the workshop. In this case, contributions should necessarily provide: (i) an introductory section that explains how the presented research fits within the Swarm Cognition approach, (ii) a summary of the most significant results obtained and (iii) a conclusion section that outlooks future studies on Swarm Cognition.
Full papers have no page limits, meaning that we accept submissions from single to several pages. Nevertheless, all authors are encouraged to explain how their work fits within the Swarm Cognition framework and contributes to the progress on the important questions identified for the workshop.

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Submissions

All formatting guidelines (including word and latex style files) and submission instructions are available on the workshop submission page: http://laral.istc.cnr.it/swarm-cognition/Submissions/

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Topics and goals

The originality of this workshop is marked by the following: (i) addressing cutting edge Swarm Cognition research issues; (ii) involving a truly interdisciplinary cooperation; (iii) hosting world leading keynote speakers in the field. This workshop is envisioned as being a first meeting on Swarm Cognition. As such, the issues which the workshop will raise are of interest to a surprisingly diverse array of specialities. In no particular order, the following come to mind:
+ Cognitive science
+ Neurosciences
+ Situated agents
+ Bounded rationality
+ Neuroeconomics
+ Evolutionary game theory
+ Cognitive ethology
+ Neural computation and distributed representations
+ Distributed computation
+ Population biology
+ Swarm intelligence
+ Reinforcement learning
+ Adaptive control
+ Cultural evolution
+ Cognitive sociology

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Dissemination

The material presented at the workshop will be fully accessible through the workshop web site. Authors of paper accepted for the "Swarm Cognition" workshop will be invited to submit in September/October 2009 an extended version for review for publication on a special issue on Swarm Cognition of the Swarm Intelligence Journal (Springer Verlag, see http://www.springer.com/11721).

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Workshop Chairs

Dr. Vito Trianni - Institute of Cognitive Sciences and Technology, CNR, Rome, Italy
Dr. Elio Tuci - Institute of Cognitive Sciences and Technology, CNR, Rome, Italy

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Program Committee

Andrea Cavagna (Centre for Statistical Mechanics and Complexity and Institute for Complex Systems, CNR-INFM, Rome, Italy)
Nikolaus Correll (Distributed Robotics Lab , MIT CSAIL, Cambridge, MA)
Iain D. Couzin (Department of Ecology & Evolutionary Biology, Princeton University, NJ)
Marco Dorigo (IRIDIA, ULB, Brussels, Belgium)
Simon Garnier (Department of Ecology & Evolutionary Biology, Princeton University, NJ)
Aldo Genovesio (Dipartimento di Fisiologia e Farmacologia, Sapienza Università di Roma, Italy)
Carlos Gershenson (VUB, Brussels, Belgium - New England Complex Systems Institute, Cambridge, MA)
Irene Giardina (Centre for Statistical Mechanics and Complexity, CNR-INFM, Rome, Italy)
Paul Graham (CCNR, Univesitiy of Sussex, Brighton, UK)
Dirk Helbing (ETH Zurich)
Takashi Ikegami (University of Tokyo, Japan)
Laurent Keller (Department of Ecology and Evolution, UNIL, Lausanne, Switzerland)
Stefano Nolfi (ISTC, CNR, Rome, Italy)
Frank Pasemann (Institute of Cognitive Science, University of Osnabrueck, Germany)
Andrew Philippides (CCNR, Univesitiy of Sussex, Brighton, UK)
Matt Schlesinger (Psychology Department, Southern Illinois University, Carbondale, IL)
Mototaka Suzuki (Department of Neuroscience, Columbia University, New York, NY)
Jun Tani (Lab. for Behavior and Dynamic Cognition, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan)
Guy Theraulaz (Centre de Recherches sur la Cognition Animale, CNRS and Université Paul Sabatier, Toulouse, France)
Steffen Wishmann (LIS, EPFL - Department of Ecology and Evolution, UNIL, Lausanne, Switzerland)

Recursive Yin Yang 4

Recursive Yin Yang 4 by ~hawmkoonstormbringer on deviantART

The basic construction of a yin-yang symbol is to take a circle and two circles of half the radius, sitting along one axis. I used this substitution recursively with a 90 deg. rotation: For the smaller circles, add two more circles of half their radius (one fourth of the original circle). Continue substituting, and you end up with this figure. The interesting thing is that not only the surfaces are fractal, but also the line separating the two colors.

The figure can carry several messages, apart from those of yin yang: e.g. there is also struggle, balance, and interaction at different scales.

This work is dedicated to our friend and compadre Dr. Igor Lugo, who recently defended his PhD.

Tentative Laws of Information

All phenomena can be described as information, so these laws try to describe general features found across all scales.

1. Law of Information Transformation. Information will potentially be transformed by interacting with other information.
2. Law of Information Propagation. Information propagates as fast as possible.
3. Law of Requisite Complexity. More complex information will require more complex agents to perceive, act on, and propagate it.
4. Law of Information Criticality. Transforming and propagating information will tend to a critical balance between its stability and its variability.
5. Law of Information Organization. Information produces constraints that regulate information production.
   

These tentative laws are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles.

More details and examples in:

• Gershenson, C. (2007). The World as Evolving Information. In Proceedings of International Conference on Complex Systems ICCS2007.