Advances in Social Computing

Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, Bethesda, MD, USA, March 30-31, 2010. Proceedings

  • Sun-Ki Chai
  • John J. Salerno
  • Patricia L. Mabry

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)

Table of contents

  1. Front Matter
  2. Stephen Eubank, Anil Vullikanti, Maleq Khan, Madhav Marathe, Chris Barrett
    Pages 1-1
  3. Lingzhi Luo, Nilanjan Chakraborty, Katia Sycara
    Pages 3-12
  4. Patrick Roos, Dana Nau
    Pages 23-31
  5. Thomas N. Wisdom, Robert L. Goldstone
    Pages 32-41
  6. Elizabeth Bruch
    Pages 42-43
  7. Paul P. Maglio, Melissa Cefkin, Peter J. Haas, Pat Selinger
    Pages 44-51
  8. Dave DeBarr, Harry Wechsler
    Pages 62-69
  9. Tina Eliassi-Rad, Keith Henderson
    Pages 70-78
  10. Richard Colbaugh, Kristin Glass, Paul Ormerod
    Pages 79-86
  11. Georgiy V. Bobashev, Robert J. Morris, William A. Zule
    Pages 97-97
  12. William H. Batchelder, Alex Strashny, A. Kimball Romney
    Pages 98-107
  13. Jiesi Cheng, Aaron Sun, Daniel Zeng
    Pages 108-117
  14. Anna Nagurney, Qiang Qiang
    Pages 138-148

About these proceedings

Introduction

Social computing is concerned with the study of social behavior and social context based on computational systems. Behavioral modeling provides a representation of the social behavior, and allows for experimenting, scenario planning, and deep und- standing of behavior, patterns, and potential outcomes. The pervasive use of computer and Internet technologies by humans in everyday life provides an unprecedented en- ronment of various social activities that, due to the platforms under which they take place, generate large amounts of stored data as a by-product, often in systematically organized form. Social computing facilitates behavioral modeling in model building, analysis, pattern mining, and prediction. Numerous interdisciplinary and interdepe- ent systems are created and used to represent the various social and physical systems for investigating the interactions between groups, communities, or nation-states. This requires joint efforts to take advantage of the state-of-the-art research from multiple disciplines improving social computing and behavioral modeling in order to document lessons learned and develop novel theories, experiments, and methodologies to better explain the interaction between social (both informal and institutionalized), psyc- logical, and physical mechanisms. The goal is to enable us to experiment, create, and recreate an operational environment with a better understanding of the contributions from each individual discipline, forging joint interdisciplinary efforts. This volume comprises the proceedings of the third international workshop on - cial Computing, Behavioral Modeling and Prediction, which has grown trem- dously.

Keywords

Open Source calculus classification data mining dynamische Systeme machine learning modeling security system modeling

Editors and affiliations

  • Sun-Ki Chai
    • 1
  • John J. Salerno
    • 2
  • Patricia L. Mabry
    • 3
  1. 1.Department of SociologyUniversity of HawaiiHonoluluUSA
  2. 2.Air Force Research LaboratoryRome Research Site, AFRL/RIEFRomeUSA
  3. 3.Department of Behavioral and Social Sciences ResearchNational Institute of Health (NIH)BethesdaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-12079-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-12078-7
  • Online ISBN 978-3-642-12079-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book