Collectives and the Design of Complex Systems (eng)

Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high­ dimensional spaces. Given the difficulty of performing such high-dimensional op­ timization with modern computers, there has been a lot of exploration of computa­ tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.

 

Increasingly powerful computers are making possible distributed systems comprised of many adaptive and self-motivated computational agents. Such systems, when distinguished by system-level performance criteria, are known as "collectives."

Collectives and the Design of Complex Systems lays the foundation for a science of collectives and describes how to design them for optimal performance. An introductory survey chapter is followed by descriptions of information-processing problems that can only be solved by the joint actions of large communities of computers, each running its own complex, decentralized machine-learning algorithm. Subsequent chapters analyze the dynamics and structures of collectives, as well as address economic, model-free, and control-theory approaches to designing complex systems. The work assumes a modest understanding of basic statistics and calculus.

Topics and Features:

Introduces the burgeoning science of collectives and its practical applications in a single useful volume

Combines approaches from known researchers in the physics, economics, game theory, biology, and artificial intelligence (A.I.) communities

Analyzes the role of information transmission in collectives, such as in the minority game, specific game theory scenarios, and computational ecologies

Presents a broad view of state-of-the-art techniques and developments in the field

Describes the benefits and promise of collectives-based design of systems, and explores recent challenges faced by researchers

Highlights the interdisciplinary value to studying and using collectives

Provides a comprehensive subject index and chapter-ending bibliographies

With an accessible style that integrates key theoretical principles with applications in real-world scenarios, this unique monograph surveys thelatest research on the dynamics of collectives, their A.I.-related aspects, and critical design issues pertaining to them. Computer scientists, computer engineers, and practitioners, researchers, and graduate students with an interest in this new and growing field will find the book an authoritative introduction and resource.