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Complex Adaptive Systems

Views from the Physical, Natural, and Social Sciences

  • Ted Carmichael
  • Andrew J. Collins
  • Mirsad Hadžikadić
Book

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Ted Carmichael, Mirsad Hadžikadić
    Pages 1-16
  3. Catherine Zanbaka, Jason HandUber, Desmond Saunders-Newton
    Pages 39-63
  4. Erika G. Ardiles Cruz, John A. Sokolowski, Timothy Kroecker, Sachin Shetty
    Pages 65-77
  5. Christopher J. Lynch, Hamdi Kavak, Ross Gore, Daniele Vernon-Bido
    Pages 129-142
  6. Megan Olsen
    Pages 169-197
  7. Joshua Cherian Varughese, Daniel Moser, Ronald Thenius, Franz Wotawa, Thomas Schmickl
    Pages 213-222
  8. Loren Demerath, E. Dante Suarez
    Pages 223-250

About this book

Introduction

​This book emerged out of international conferences organized as part of the AAAI Fall Symposia series, and the Swarmfest 2017 conference. It brings together researchers from diverse fields studying these complex systems using CAS and agent-based modeling tools and techniques. In the past, the knowledge gained in each domain has largely remained exclusive to that domain. By bringing together scholars who study these phenomena, the book takes knowledge from one domain to provide insight into others.

Most interesting phenomena in natural and social systems include constant transitions and oscillations among their various phases –  wars, companies, societies, markets, and humans rarely stay in a stable, predictable state for long. Randomness, power laws, and human behavior ensure that the future is both unknown and challenging. How do events unfold? When do they take hold? Why do some initial events cause an avalanche while others do not? What characterizes these events? What are the thresholds that differentiate a sea change from a non-event?

Complex adaptive systems (CAS) have proven to be a powerful tool for exploring these and other related phenomena. The authors characterize a general CAS model as having a large number of self-similar agents that: 1) utilize one or more levels of feedback; 2) exhibit emergent properties and self-organization; and 3) produce non-linear dynamic behavior. Advances in modeling and computing technology have led not only to a deeper understanding of complex systems in many areas, but they have also raised the possibility that similar fundamental principles may be at work across these systems, even though the underlying principles may manifest themselves differently.

Keywords

Agent-based Modeling Complex Adaptive Systems Complexity Swarm Intelligence Computational Social Science

Editors and affiliations

  • Ted Carmichael
    • 1
  • Andrew J. Collins
    • 2
  • Mirsad Hadžikadić
    • 3
  1. 1.Complex Systems Institute, Department of Software and Information SystemsCollege of Computing and Informatics, University of North Carolina at CharlotteCharlotteUSA
  2. 2.Department of Engineering Management and Systems Engineering, College of Engineering and TechnologyOld Dominion UniversityNorfolkUSA
  3. 3.Complex Systems Institute, Department of Software and Information SystemsCollege of Computing and Informatics, University of North Carolina at CharlotteCharlotteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-20309-2
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-20307-8
  • Online ISBN 978-3-030-20309-2
  • Series Print ISSN 1860-0832
  • Series Online ISSN 1860-0840
  • Buy this book on publisher's site