Showing posts from September, 2010

CfP: Special Issue on Complex Networks, Artificial Life

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

Determinism != Predictability

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

Paper published: Computing Networks: A General Framework to Contrast Neural and Swarm Cognitions

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