Most of biological and social networks reflect topological properties not observed within simple networks (regular, random). Two examples are small-world and scale-free networks.
Published in Chapter:
Complex Systems Modeling by Cellular Automata
Jirí Kroc (Section Computational Science, The University of Amsterdam, The Netherlands) and
Peter M.A. Sloot (University of Amsterdam, The Netherlands)
Copyright: © 2009
|Pages: 8
DOI: 10.4018/978-1-59904-849-9.ch054
Abstract
In recent years, the notion of complex systems proved to be a very useful concept to define, describe, and study various natural phenomena observed in a vast number of scientific disciplines. Examples of scientific disciplines that highly benefit from this concept range from physics, mathematics, and computer science through biology and medicine as well as economy, to social sciences and psychology. Various techniques were developed to describe natural phenomena observed in these complex systems. Among these are artificial life, evolutionary computation, swarm intelligence, neural networks, parallel computing, cellular automata, and many others. In this text, we focus our attention to one of them, i.e. ‘cellular automata’. We present a truly discrete modelling universe, discrete in time, space, and state: Cellular Automata (CAs) (Sloot & Hoekstra, 2007, Kroc, 2007, Sloot, Chopard & Hoekstra, 2004). It is good to emphasize the importance of CAs in solving certain classes of problems, which are not tractable by other techniques. CAs, despite theirs simplicity, are able to describe and reproduce many complex phenomena that are closely related to processes such as self-organization and emergence, which are often observed within the above mentioned scientific disciplines.