Complexes

The Computer Science Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México  (UNAM) has a open call for research professors. Located in the heart of the UNAM's Ciudad Universitaria, a UNESCO World Heritage site, the IIMAS has been the leader in computer science in Mexico since the first computer in the country was acquired by UNAM. Researchers at UNAM have a privileged position for several reasons. UNAM is the highest ranked spanish speaking higher education institution in the world and produces half of the research in Mexico and is the largest in the continent (300K+ students). Professors in faculties do more teaching than research, while researchers in institutes (such as IIMAS) do more research than teaching (about 48 hours per year, usually to the best graduate students in the country. Groups of more than five students get a teaching assistant). Students in most graduate programs at UNAM ...

//Please forward to whom may be interested. The National Autonomous University of Mexico (UNAM) has an open call for postdoctoral fellowships to start in March, 2014 (with a close deadline!). Candidates should have obtained a PhD degree within the last three years and be under 36 years, both to the date of the beginning of the fellowship. The area of interests of candidates should fall within complex systems, artificial life, information, evolution, cognition, robotics, and/or philosophy. Interested candidates should send CV and a tentative project (1 paragraph) to cgg-at-unam.mx by Friday, August 2nd.   Full application package should be ready by Monday, August 5 at noon, Mexico City time. Projects can be inspired from:  http://turing.iimas.unam.mx/~cgg/projects.html Postdoctoral fellowships are between one and two years (after renewal). Spanish is not a requisite. Accepted candidates would be working at the Self-organizing Systems Lab ( http://turing.iimas.unam....

In this chapter, concepts related to information and computation are reviewed in the context of human computation. A brief introduction to information theory and different types of computation is given. Two examples of human computation systems, online social networks and Wikipedia, are used to illustrate how these can be described and compared in terms of information and computation. Full text at  http://arxiv.org/abs/1304.1428 Draft of a chapter to be published in Michelucci, P. (Ed.) Handbook of Human Computation , Springer.

In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the...

Self-organization in the context of computing systems refers to a technological approach to deal with the increasing complexity associated with the deployment, maintenance, and evolution of such systems. The terms self-organizing and autonomous are often used interchangeably in relation to systems that use organic principles (self-configuration, self-healing, and so on) in their design and operation. In the specific case of system on chip (SoC) design, organic principles are clearly in the solution path for some of the most important challenges in areas like logic organization, data movement, circuits, and software. In this article, we start by providing a definition of the concept of self-organization as it applies to SoCs, explaining what it means and how it may be applied. We then provide a survey of the various recent papers, journal articles, and books on the subject and close by pointing out possible future directions, challenges and opportunities for self-organizing SoCs. De ...

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...

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 c...