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ISCS 2013: Interdisciplinary Symposium on Complex Systems

Part of the Emergence, Complexity and Computation Book Series (volume 8)

Editors: Sanayei, Ali; Zelinka, Ivan; Rössler, Otto E.

DOI: 10.1007/978-3-642-45438-7

ISBN: 978-3-642-45437-0

Publisher: Springer Berlin Heidelberg - Springer Journals

Feb 16, 2014

1 - 10 of 39 Chapters

Available

The Complex Geometry of the Mandelbrot Set

Devaney, Robert L.

Pages: 3 –8

[In this paper, we give a brief overview of the geometry of the Mandelbrot set. We show how to distinguish each of the principal bulbs hanging off the main cardioid of this set by counting the spokes of the antennas attached to each bulb. We also use these antennas to attach a fraction to each...
Available

Is There a World Behind Shannon? Entropies for Complex Systems

Thurner, Stefan; Hanel, Rudolf

Pages: 9 –18

[In their seminal works, Shannon and Khinchin showed that assuming four information theoretic axioms the entropy must be of Boltzmann-Gibbs type, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb}...
Available

Complex Systems Science: From Cell Regulation to the Global Food Crisis

Bar-Yam, Yaneer

Pages: 19 –28

[Insights and methods of complex systems science are transforming science and providing clarity about the impact of policies to address major societal problems. These conceptual and mathematical advances allow us to study interdependence, patterns, networks, multiscale behaviors, and “big data.”...
Available

Hidden Complexity of Evolutionary Dynamics: Analysis

Zelinka, Ivan; Skanderova, Lenka; Saloun, Petr; Senkerik, Roman; Pluhacek, Michal

Pages: 29 –46

[This chapter presents a method for visualization of the dynamics of evolutionary algorithms in the form of complex networks and is continuation of our previous research. The analogy between individuals of populations in an arbitrary evolutionary algorithm and vertices of a complex network is...
Available

The Brain Equation

Rössler, Otto E.

Pages: 47 –56

[The brain equation is a solution to the “second survival problem.” The latter is called “positional adaptation.” It unlike Darwin’s first (“metabolic adaptation”) is history-independent. As such it is mathematically well posed. The equation applies to all life forms in the cosmos that live in a...
Available

Nature Versus Nurture in Complex and Not-So-Complex Systems

Stein, D. L.; Newman, C. M.

Pages: 57 –63

[Understanding the dynamical behavior of many-particle systems both in and out of equilibrium is a central issue in both statistical mechanics and complex systems theory. One question involves “nature versus nurture”: given a system with a random initial state evolving through a well-defined...
Available

Complex Self-Reproducing Systems

Edwards, Roderick; Maignan, Aude

Pages: 65 –76

[Cellular automata and L-Systems are well-known formal models to describe the behaviour of biological processes. They are discrete dynamical systems, each of which can have complex and varied behaviour. Here, we study a class of substitutive systems incorporating properties of both cellular...
Available

On Fundamentals of Global Systems Control Science (GSCS)

Jasinevicius, Raimundas; Petrauskas, Vytautas

Pages: 77 –87

[Globalization leads us towards dealing with very complex systems that consist of evolving, overlapping, and interacting “socio-technical fabrics”. An existing general systems control theory cannot cope with problems occurring in such systems. This chapter is, first of all, an attempt to present...
Available

Emergent Phenomena in Natural Complex Systems

Bila, Jiri

Pages: 89 –100

[In this chapter are described some properties of emergent phenomena and situations that appear in natural complex systems. There are introduced three classes of unexpected situations and two of them belong to emergent situations. For the detecting possibility of appearance of emergent situation...
Available

Evolutionary Systems in Complex Signal Analysis

Brandejsky, Tomas

Pages: 101 –108

[All complex systems presenting chaotic behaviour are non-linear ones and many problems of their analysis and modelling are caused by application of linear or pseudo-linear models which are not able to represent all aspects of signals generated by these systems. Experiments with some...

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