CfP: Special Issue on Complex Networks, Artificial Life

Call for Papers
Special Issue on Complex Networks
Artificial Life Journal
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 distributions observed in many real-world networks.

Another direction is to investigate network motifs and subgraphs in order to understand and analyse the local structure and function of networks. The presence of a certain motif in a network may mean that that motif plays an important role in the overall functionality of the network. Thus, functionality of specific motifs, including their information processing and control functions, is a challenging topic relevant in Artificial Life studies, such as genetic regulatory networks, cell signaling networks, and protein interaction networks.

In addition, propagation and processing of information within networks may be analysed as (Shannon) information dynamics. Such analysis requires to consider not only networks' topology, but also the time-series dynamics at individual nodes. Specific topics of interest include phase transitions of network properties between ordered and chaotic regimes, where information transfer is often maximised, and other nonlinear phenomena related to criticality in networks. 

The intention of the special issue is to bring together research from both Artificial Life and Complex Networks communities, in order to facilitate cross-fertilization, increase exposure of both communities to relevant research and foster new collaborations.
Contributions to the Session should be prepared and submitted according to the Artificial Life journal guidelines, available at http://www.mitpressjournals.org/page/sub/artl. Authors should also include a cover letter describing briefly the relevance of their article to the specific topic of this call. Every submission will be subject to full peer review.

Articles should NOT be submitted to the journal editor, but should be uploaded through the special issue website (TBA).

Papers will be judged by members of the Program Committee on their relevance to the call for papers, originality, clarity of the presentation, and overall quality.

Important Dates
Paper submission: December 15th, 2010
Paper notification: February 28th, 2011
Camera-ready papers due: March 31st, 2011

Program Committee

Guest Editors

Dr. Mikhail Prokopenko
CSIRO, Australia

Dr. Carlos Gershenson


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 information is not included in the initial nor boundary conditions. Since you do not know how the system will interact, the only way to know the future state of a system is by "running it". Of course, a posteriori you can make predictions. This is known as "computational irreducibility": you know the "laws" of a system, but you need to compute the trajectory of an initial state before you can know what will be a future state. This is also related to the halting problem. Just an example:
ECA rule 110 is very simple to describe. However, you cannot deduce a priori all the different dynamic structures (aka gliders) that emerge from the interactions. Moreover, you cannot infer from the rules of the ECA the result of the glider interactions. You need to compute them and see. Even more, there is no chance you could prove from the rules of the ECA that it is capable of universal computation. Same arguments apply for the Game of Life.


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

Gershenson, 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.


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 computation is discussed.
Keywords  cognition - computation - neural architecture - swarm architecture - swarm cognition - multiple scales