Complexes

Facebook offers great functionalities, it is easy, fun, extensible... However, it seems that like many things which are positive for many people (e.g. automobiles), they get overused with unintended consequences (e.g. traffic jams). OK, so I have 400+ friends on facebook. The problem is that I could spare like 5 minutes every other day to check the news feed. With so many people in my feed, I get what was posted 2 hours ago at most. Sure, there are ways of blocking applications, creating filters (e.g. with Better FB ), but this does not work for me. There are just too many posts I am not interested about, but I am interested in few things that most people post about. It is difficult to categorize. Where is artificial intelligence when it is needed? I believe that algorithms similar to anti-spam filters would be immensely useful on social networks. For example, I am interested about the English postings of my Iranian friends, but I cannot make much of their Farsi posts... For users, i

Call for Papers Special Issue on Complex Networks Artificial Life Journal Motivation As a result of the quality of the Complex Networks track at the ALife XII conference last August in Odense, Denmark and the interest of the attendants; we announce a call for papers for a special issue on this theme for the Artificial Life Journal. Many complex systems are amenable to be described as networks. These include genetic regulatory, structural or functional cortical networks, ecological systems, metabolism of biological species, author collaborations, interaction of autonomous systems in the Internet, etc. A recent trend suggests to study common  global  topological features of such networks, e.g. network diameter, clustering coefficients, assortativity, modularity, community structure, etc. Various network  growth models  have also been proposed and studied to emulate the features of the real-world networks, e.g. the preferential attachment model, which explains scale-free power law degre

Gershenson, C. (2010). The sigma profile: A formal tool to study organization and its evolution at multiple scales. Complexity,  first published online: 10 NOV 2010. DOI: 10.1002/cplx.20350 Abstract The σ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal-oriented agents, one can define σ ∈ [0,1] (satisfaction) as the degree to which the goals of each agent at each scale have been met. σ reflects the organization degree at that scale. The σ profile of a system shows the satisfaction at different scales, with the possibility to study their dependencies and evolution. It can also be used to extend game theoretic models. The description of a general tendency on the evolution of complexity and cooperation naturally follows from the σ profile. Experiments on a virtual ecosystem are used as illustration. Full text http://dx.doi.org/10.1002/cplx.20350 (se

The science: mice are born with genetic material of two males (fresh news). http://blogs.nature.com/news/thegreatbeyond/2010/12/look_dads_no_mum.html The science fiction: dreams of a teenager (~12 years ago). http://turing.iimas.unam.mx/~cgg/jlagunez/npm1.htm  [in Spanish...]

Call for Papers Artificial Life, Robotics, Evolvable Hardware Track @ GECCO 2011 Genetic and Evolutionary Computation Conference July 12-16, Dublin, Ireland http://www.sigevo.org/gecco-2011/ This track promotes evolutionary computation and bio-inspired heuristics as instruments able to face engineering problems and scientific questions in different areas that include (but are not limited to): artificial life, robotics, and evolvable hardware. Artificial life studies artificial systems (software, hardware, or chemical) with properties similar to those of living systems. There are two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties of living systems, such as adaptability, evolvability, active perception, communication, organization. Evolutionary computation techniques can be particularly useful for a large branch of robotics. The evolution of controllers, morphologies, sensors, and communica

Call for Papers & Workshop Proposals Eighth International Conference on Complex Systems (ICCS 2011) June 26 - July 1, 2011 Boston Marriott, Quincy, MA, USA http://necsi.edu/events/iccs2011/ * - * - * - * - * - * - * - * - * Host: New England Complex Systems Institute ( http://necsi.edu/ ) This is the eighth in a series of conferences with two major aims: first, to investigate those properties or characteristics that appear to be common to the very different complex systems now under study; and second, to encourage cross fertilization among the many disciplines involved. - - - - - - - - - - ICCS TOPICS: UNIFYING THEMES IN COMPLEX SYSTEMS Sessions will be structured around both themes and systems. The themes are: EMERGENCE: The relationship of component to collective behavior; the relationship of internal structure to external influence; multiscale structure and dynamics; self-similarity and fractals. COMPLEXITY & INFORMATION: Defining complexity; c

The proceedings of ICCS2007 will be published soon, I believe that as a volume in the Springer "Understanding Complex Systems" series. With this excuse, I updated this paper with three more laws of information, two new classifications, and further discussions. Any feedback is more than welcome. Gershenson, C. (In Press). The World as Evolving Information. To be published in Proceedings of ICCS 2007. Abstract : This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents eight tentative laws of information, valid at multiple

Call for Papers Special Issue on Complex Networks Artificial Life Journal Motivation As a result of the quality of the Complex Networks track at the ALife XII conference last August in Odense, Denmark and the interest of the attendants; we announce a call for papers for a special issue on this theme for the Artificial Life Journal. Many complex systems are amenable to be described as networks. These include genetic regulatory, structural or functional cortical networks, ecological systems, metabolism of biological species, author collaborations, interaction of autonomous systems in the Internet, etc. A recent trend suggests to study common  global  topological features of such networks, e.g. network diameter, clustering coefficients, assortativity, modularity, community structure, etc. Various network growth models have also been proposed and studied to emulate the features of the real-world networks, e.g. the preferential attachment model, which explains scale-free power law degree

Many people assumed that if a system is deterministic, it should be predictable. Chaos and complexity each show different situations where this fails to hold. In deterministic chaos, even when you know precisely the "laws" of a system, its extreme sensitivity to initial conditions (formally described with positive Lyapunov exponents) implies that sooner or later, very similar initial states will tend to very different states, since trajectories diverge exponentially. OK, some people may argue that if we had infinite precision, then we could predict precisely the future, so it is just a practical nuisance that in theory should work (I have no idea how, but anyway... people are stubborn (not me! I am just self-confident!)). But you cannot get away with lack of predictability that is inherent of complexity. Within a complex system, yes even with deterministic rules, interactions between components generate novel information that determines the future of the system. This info

G ershenson, C. (2010). Computing Networks: A General Framework to Contrast Neural and Swarm Cognitions,  Paladyn, Journal of Behavioral Robotics   1 (2): 147-153, DOI: 10.2478/s13230-010-0015-z . Abstract This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computatio

I'll give a keynote talk at the First Mexican Congress on Complexity Sciences, to be held at the University City, within Mexico City, October 4-6, 2010. This first edition is aimed at the Mexican community, so the language is Spanish. More info at:  http://c3.fisica.unam.mx/con2010/ Abstract deadline for oral presentations: August 15th Abstract deadline for posters: August 31st (Students presenting talks or posters have the registration fee waived!)

I'll be co-chairing together with Giovanni Squillero  the Artificial Life/Robotics/Evolvable Hardware track at the Genetic and Evolutionary Computation (GECCO 2011) conference, to be held  in Dublin, Ireland, July 12-16, 2011. More info at the conference website:  http://www.sigevo.org/gecco-2011/

I'll be giving a talk at the GSO-3 workshop at Indiana University next september. More info at:  http://informatics.indiana.edu/larryy/gso3/ You can also assist and present, deadline for abstracts is August 16th. Registration is free, but space is limited.

I have been cycling as my main mean of transport for more than ten years. I've had the opportunity to bike in Mexico City (MX), St. Petersburg (RU), Sussex (UK), Brussels (BE), and Boston (MA, USA). I have a childseat for our 2.5 year old daughter, she loves it, and it is quite comfortable. I do not suggest biking to anybody, I just share my experience for people who have made a similar decision. Similarly, I would like to hear from other people's experience. Different places have different degrees of bike-friendliness, even in the same city, depending on several factors. In my experience, the order from best to worse is: UK, BE, USA, MX, RU. Actually, there seems to be a correlation with the number of bikers on the street. Cities with more bikers tend to be friendlier towards them, and friendlier cities tend to attract more bikers. Conclusion: promote biking to make your city bike-friendlier. Pros Speed . Studies from Mexico City and Paris have shown that biking leads to th

I am co-chairing the technical programme committe, together with Christian Bettstetter , IWSOS 2011, the Fifth International Workshop on Self-Organizing Systems, to be held on February, 23-25, 2011 in Karlsruhe, Germany. The general co-chairs are Martina Zitterbart and Hermann de Meer . Call for Papers will be distributed soon. Check the webpage at http://iwsos2011.tm.kit.edu

My BEng thesis (from 2001) was just published as a book. You can order a hardcopy at Amazon.com . It is still available electronically in pdf and html . Gershenson, C. (2010 ). Artificial Societies of Intelligent Agents: Virtual Experiments of Individual and Social Behaviour . LAP Lambert Academic Publishing, ISBN 3838357736 . Summary : In this book we use artificial societies to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in virtual animats.

Gershenson, C. (2010). Guiding the Self-organization of Random Boolean Networks. C3 Report 2010.05. Abstract : Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews seven different methods for guiding the self-organization of RBNs. In particular, the article is focussed on guiding RBNs towards the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are revised. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical. Full text : http://uk.arxiv.org/abs/1005.5733

I did the entry of " Complexity " for the upcoming Encyclopedia of Astrobiology (Springer). You can read the entry here .

My thesis was just published by LAP. This might be useful for anybody wanting a hardcopy of it. If you prefer printing it or reading electronically, it is still available here . Gershenson, C. (2010). Design and Control of Self-organizing Systems: Facing Complexity with Adaptation and Self-organization . LAP. Summary : Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves. A general introduction to complex thinking is given, while practical notions of complexity and self-

This seems like a great site for sharing your slides... http://www.slideshare.net/ Conference organizers can upload the slides of their event to increase its impact... Should upload the ones of my talks and my course .

http://cloud.ld.ttu.ee/cloud/ CALL FOR CHAPTERS Proposals Submission Deadline: 3/15/2010 Full Chapters Due: 7/15/2010 Agile and Self-Organizing Enterprise Information Systems: Developing a Cloud Platform A book edited by Dr. Enn Õunapuu, Tallinn University of Technology, Estonia Introduction We are moving to a new era with rapid changes. The business context is changing rapidly and we need to change our information system very quickly. We have to change the way we create Enterprise Information Systems. The problem is that despite Moore’s law, (Kurzweil, 2005) the capability of computers doubles every eighteen months, the programs and their creation are becoming slower and slower (Wirth’s law– Software is getting slower more rapidly than hardware becomes faster). In summary – the creation of information systems is slow and very costly. This is not the only problem with our information systems. The second problem with created information systems is that in many cases they fulfill func

Feedback, suggestions, and criticisms are more than welcome. Gershenson, C. (2010) "Computing Networks: A General Framework to Contrast Neural and Swarm Architectures". C3 Report No. 2010.01. Abstract : Computing Networks (CNs) are defined. These are used to generalize neural and swarm architectures, namely artificial neural networks, ant colony optimization, and particle swarm optimization. The description of these architectures as CNs allows their comparison, distinguishing which properties enable them to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. Full paper : http://uk.arxiv.org/abs/1001.5244

Today Lama Jampa visited our sangha , giving several teachings. He mentioned (or at least Eduardo translated it that way from Tibetan) that when one is in the state of the Buddha, things are perceived as they really are. Since I had learned from Eduardo that in Buddhism things are composed by object, subject, and action, I did not understand the meaning of Lama Jampa's words, so I asked. My translated summary of what I understood of the translation of Lama Jampa's summary is the following: You cannot really see things (they are in between being, not being, being and being, and not {being and not being}). The phrase "things as they really are" means that one realizes causality and relationships between all things, i.e. that no thing is isolated. I find this very interesting, because this is precisely what the scientific study of complex systems does, as opposed to traditional reductionistic science (since Galileo, Descartes, Newton, Laplace). We study the relevance of

//Please redistribute //Apologies for multiple copies Call for Papers Session on Complex Networks at ALife XII: 12th International Conference on the Synthesis and Simulation of Living Systems Odense, Denmark, 19-23 August 2010 Coordinators: Mikhail Prokopenko and Carlos Gershenson http://www.prokopenko.net/ComplexNetworks.html Motivation Many complex systems are amenable to be described as networks. These include genetic regulatory, structural or functional cortical networks, ecological systems, metabolism of biological species, author collaborations, interaction of autonomous systems in the Internet, etc. A recent trend suggests to study common global topological features of such networks, e.g. network diameter, clustering coefficients, assortativity, modularity, community structure, etc. Various network growth models have also been proposed and studied to emulate the features of the real-world networks, e.g. the preferential attachment model, which explains scale-free power law degr

My wife Nadya started working as a wellness coach a few months ago. I have received great benefits from her recently acquired knowledge. We already had "healthy" diets, we had "normal" weights, but it turns out that we could do much better. In few weeks, with the help of the latest "nutrition technology", we got much more energy (useful when an active toddler is jumping around...), had much better digestion, recovered the weight we had when we were teens... (I recovered the muscles I had when I swam 50Km a week, but I barely exercise: nutrition seems to be more important than physical activity for health (which is also important)). If you would like to know more about how to feel better and improve your health through nutrition, feel free to contact my wife nadya.bienestar@gmail.com Now, why most people feed on "dark age" food, instead of receiving benefits from the latest scientific discoveries in nutrition? Well, many people simply inherit thei