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Social Self-Organization

Agent-Based Simulations and Experiments to Study Emergent Social Behavior

  • Dirk Helbing

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Dirk Helbing
    Pages 1-24
  3. Dirk Helbing
    Pages 25-70
  4. Dirk Helbing
    Pages 71-99
  5. Dirk Helbing
    Pages 101-114
  6. Dirk Helbing
    Pages 131-138
  7. Dirk Helbing
    Pages 153-167
  8. Dirk Helbing
    Pages 201-209
  9. Dirk Helbing
    Pages 211-237
  10. Dirk Helbing
    Pages 239-259
  11. Dirk Helbing
    Pages 261-284
  12. Dirk Helbing
    Pages 285-299
  13. Dirk Helbing
    Pages 301-329
  14. Back Matter
    Pages 331-340

About this book

Introduction

What are the principles that keep our society together? This question is even more difficult to answer than the long-standing question, what are the forces that keep our world together. However, the social challenges of humanity in the 21st century ranging from the financial crises to the impacts of globalization, require us to make fast progress in our understanding of how society works, and how our future can be managed in a resilient and sustainable way. This book can present only a few very first steps towards this ambitious goal. However, based on simple models of social interactions, one can already gain some surprising insights into the social, ``macro-level'' outcomes and dynamics that is implied by individual, ``micro-level'' interactions. Depending on the nature of these interactions, they may imply the spontaneous formation of social conventions or the birth of social cooperation, but also their sudden breakdown. This can end in deadly crowd disasters or tragedies of the commons (such as financial crises or environmental destruction). Furthermore, we demonstrate that classical modeling approaches (such as representative agent models) do not provide a sufficient understanding of the self-organization in social systems resulting from individual interactions. The consideration of randomness, spatial or network interdependencies, and nonlinear feedback effects turns out to be crucial to get fundamental insights into how social patterns and dynamics emerge. Given the explanation of sometimes counter-intuitive phenomena resulting from these features and their combination, our evolutionary modeling approach appears to be powerful and insightful. The chapters of this book range from a discussion of the modeling strategy for socio-economic systems over experimental issues up the right way of doing agent-based modeling.  We furthermore discuss applications ranging from pedestrian and crowd dynamics over opinion formation, coordination, and cooperation up to conflict, and also address the response to information, issues of systemic risks in society and economics, and new approaches to manage complexity in socio-economic systems.

Parts of this book were previously published in peer reviewed journals.

Keywords

Agent Based Modelling Behaviour Social Networks Computational Social Science Emergent Social Beahiour Innovation Spreading Networks Managing Complexity Opinion Formation Social System Risks Society Economics Self Organization Crowds Socio-economic Systems

Editors and affiliations

  • Dirk Helbing
    • 1
  1. 1.Lehrstuhl Soziologie, CLU E1ETH ZürichZürichSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-24004-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-3-642-24003-4
  • Online ISBN 978-3-642-24004-1
  • Series Print ISSN 1860-0832
  • Series Online ISSN 1860-0840
  • Buy this book on publisher's site