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Collectives and the Design of Complex Systems

  • Book
  • © 2004

Overview

Editors:
  1. Kagan Tumer

    1. Computational Sciences Division, NASA Ames Research Center, Moffett Field, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. David Wolpert

    1. Computational Sciences Division, NASA Ames Research Center, Moffett Field, USA

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    You can also search for this editor in PubMed   Google Scholar

  • First book to seriously introduce collectives and their potential applications in a single volume

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xi
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  2. A Survey of Collectives

    • Kagan Tumer, David Wolpert
    Pages 1-42

  3. Theory of Collective Intelligence

    • David Wolpert
    Pages 43-106

  4. On Learnable Mechanism Design

    • David C. Parkes
    Pages 107-131

  5. Asynchronous Learning in Decentralized Environments: A Game-Theoretic Approach

    • Eric J. Friedman
    Pages 133-143

  6. Competition between Adaptive Agents: Learning and Collective Efficiency

    • Damien Challet
    Pages 145-160

  7. Managing Catastrophic Changes in a Collective

    • David Lamper, Paul Jefferies, Michael Hart, Neil F. Johnson
    Pages 161-183

  8. Effects of Interagent Communications on the Collective

    • Zoltán Toroczkai, Marian Anghel, György Korniss, Kevin E. Bassler
    Pages 185-198

  9. Man and Superman: Human Limitations, Innovation, and Emergence in Resource Competition

    • Robert Savit, Katia Koelle, Wendy Treynor, Richard Gonzalez
    Pages 199-211

  10. Design Principles for the Distributed Control of Modular Self-Reconfigurable Robots

    • Arancha Casal, Tad Hogg
    Pages 213-230

  11. Two Paradigms for the Design of Artificial Collectives

    • Kristina Lerman, Aram Galstyan
    Pages 231-256

  12. Efficiency and Equity in Collective Systems of Interacting Heterogeneous Agents

    • Akira Namatame, Saori Iwanaga
    Pages 257-276

  13. Selection in Coevolutionary Algorithms and the Inverse Problem

    • Sevan Ficici, Ofer Melnik, Jordan Pollack
    Pages 277-294

  14. Dynamics of Large Autonomous Computational Systems

    • Tad Hogg, Bernardo A. Huberman
    Pages 295-315

  15. Back Matter

    Pages 317-322
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Keywords

About this book

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.

Reviews

From the reviews:

"From the cybernetics point of view complex systems embrace a great number of disciplines … . this text is concerned with a computer-science viewpoint and addresses issues in the design of complex systems. It is presented as a collection of chapters which although independent make up a readable whole. The book is highly recommended by several writers and reviewers." (C.J.H. Mann, Kybernetes: The International Journal of Systems & Cybernetics, (34) 5, 2005)

Editors and Affiliations

  • Computational Sciences Division, NASA Ames Research Center, Moffett Field, USA

    Kagan Tumer, David Wolpert

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