Emergent Properties in Natural and Artificial Dynamical Systems

“The aim of this book is to study emergent properties arising through dynamical processes in various types of natural and artificial systems. It is related to multidisciplinary approaches in order to obtain several representations of complex systems using various methods to extract emergent structures.

Complex systems are a new scientific frontier which has emerged in the past decades with the advance of modern technology and the study of new parametric fields in natural systems. Emergence besides refers to the appearance of higher levels of system properties and behaviour that even if obviously originated in the collective dynamics of system's components are neither found in nor directly deductible from the lower level properties of this system. Emergent properties are properties of the ¡®whole' not possessed by any of the individual parts making up this whole. Self-organization is one of the major conceptual keys to study emergent properties in complex systems.

Many scientific fields are concerned with self-organization for many years. Each scientific field describes and studies these phenomena using its own specific tools and concepts. In physics, self-organized vortex formation (von Karman streets), sand ripples, etc, are observed for a long time. Biology and life sciences are probably the major domains where self-organization regularly appears, like in immune system, neuronal systems, social insects, etc. Mathematics develops studies on the formalisation of self-organized order, for instance in synchronized chaotic systems. Computer science develops innovative models using distributed systems like multiagent systems or interactive networks, where some kind of dynamical organizations are detected during the simulations. Chemistry proposes a formalisation of dissipative structures on open and complex systems. Dissipative structures become a basis of conceptual developments towards some general theories that define the new science of complexity.

This book is organized as follows. In the first part, we introduce some general approaches on complex systems and their trajectories control. We address the important problem of controlling a complex system, because there is now evidence in industrial applications that transforming a complicated man-made system into a complex one is extremely beneficial as far as performance improvement is concerned. A well defined control law can be set so that a complex system described in very general terms can be made to behave in a prescribed way. Handling systems self-organization when passing from complicated to complex, rests upon the new paradigm of passing from classical trajectory space to more abstract task space.

The second part introduces natural system modeling. Bio-inspired methods and social insect algorithms are the tools for studying emergent organizations. Self-organized fluid structures are presented for ecosystem modeling. Automata-based models allow to simulate the dectected structures in terms of stabilization. DNA patterns of self-organization in complex molecular systems are presented.

In the third part we address chaotic dynamical systems and synchronization problem. An innovative geometrical methodology is given for modeling complex phenomena of oscillatory burst discharges that occur in real neuronal cells. Emergence of complex (chaotic) behaviour in synchronized (non chaotic) dynamical systems is also described.

The fourth part focuses on decision support systems. Automata-based systems for adaptive strategies are developed for game theory. This model can be generalized for self-organization modeling by genetic computation. Adaptive decentralized methods for medical system diagnosis are presented as emergent properties from a multiagent system. Outlines of a decision support system for agricultural water management are described as emergent results from complex interactions between a multiagent system and a constraint programming system.

Finally, part 5 and 6 deal with technological complex systems leading to invariable properties. This invariance which is an emergent result charaterizes self-organization. Spline functions are shown to be efficient and adapted formal tools to study and control these invariance properties in technological complex systems. Methodologies are given to control complex systems, by considering their whole behaviour, using feed-back processes based on final output analysis. Applicative studies on flexible robotic systems are shown.”