Abstract
Self-organized criticality (SOC) is a measure to identify if complex systems have the potential to build out emergent behavior. This phenomenon is known in many different areas of physics, biology, ecology, economy and social systems. Its core assertion is that an over critical energy input can lead to spontaneous, self-enforcing and unpredictable self-organization. In such a process new structures and orders emerge. “Online Crowds” are masses of internet users who behave and act collectively on the web. They tend to follow specific online trends and can generate tremendous online traffic and attention (cp. Social software like YouTube.com, MySpace.com, Friendster.com, etc.). “Online Crowds” are observed rarely, are hard to predict and even harder to generate artificially. So it is essential for online business ideas to foster and facilitate these “Online Crowds” to reach a critical mass of online users to grow continuously and to produce profit. The paper in hand introduces the “Online Crowds” model (OCM) in context of web information systems employing the concepts of self organization theory and self organization criticality. Furthermore a statistical approach for detection of indicators of self organized criticality is presented along with experiments and preliminary results showing the applicability of the approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arthur, W.B.: Increasing Returns and Path Dependence in the Economy. University of Michigan Press, Ann Arbor (1994)
Banzhaf, W.: Overview of self-organzing systems in Science. In: Encyclopedia of Physical Science and Technology, 14th edn., pp. 589–598. Academic Press, New York (2002)
Bak, P., Tang, C., Wiesenfeld, K.: Self-Organized Criticality: An Explanation of 1/f Noise. Physical Review Letters 59, 381–384 (1987)
Barabási, A.L., Bonabeau, E.: Scale-free networks. Scientific American 288, 60–69 (2003)
Beckenkamp, M.: The Herd Moves? MPI Collective Goods Preprint No. 14 (2006)
Bikhchandani, S., Hirshleifer, D., Welch, I.: Learning from the Behavior of Others: Conformity, Fads, and Information Cascades. Journal of Economic Perspectives 12(3), 151–170 (1998)
Bonabeau, E.: The Perils of the Imitation Age. Harvard Business Review, 49–54 (2004)
Bughin, J.R.: How the companies can make the most of user-generated content. The McKinsey Quarterly (2007)
Celen, B., Kariv, S.: Observational learning under imperfect information. Games and Economic Behavior 47(1), 72–86 (2004)
Colman, A.: A Dictionary of Psychology. Oxford University Press, Oxford (2001)
Fuchs, C.: The Self-Organization of Virtual Communities. Journal of New Communications Research 1(1), 29–68 (2006)
Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Difference. Back Bay Books, Boston (2002)
Golder, S.A., Huberman, B.A.: The Structure of Collaborative Tagging Systems. Journal of Information Science 32(2), 198–208 (2006)
Gross, R., Acquisti, A., Heinz, I.H.J.: Information revelation and privacy in online social networks. In: ACM Workshop on Privacy in the Electronic Society 2005, pp. 71–80. ACM Press, New York (2005)
Heylighen, F.: The Science of Self-organization and Adaptivity. In: Kiel, L.D. (ed.) Knowledge Management, Organizational Intelligence and Learning, and Complexity, Eolss Publishers, Oxford (2001)
Knowledge & Data Engineering Group, University of Kassel: Benchmark Folksonomy Data from BibSonomy. Kassel (2006)
Katz, M.L., Shapiro, C.: Network Externalities, Competition, and Compatibility. The American Economic Review 75(3), 424–440 (1985)
Kurihara, S., Sugawara, T., Onai, R.: Self-organization based on nonlinear nonequilibrium. Dynamics of autonomous agents. Artificial Life and Robotics 2(3), 102–107 (1998)
Ossimitz, G., Lapp, C.: Das Metanoia Prinzip — Eine Einführung in systemgerechtes Denken und Handeln. Franzbecker Verlag, Hildesheim (2006)
O’Reilly, T.: What Is Web 2.0. Design Patterns and Business Models for the Next Generation of Software (2005), http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html
Mika, P.: Ontologies Are Us: A Unified Model of Social Networks and Semantics. In: International Semantic Web Conference, pp. 522–536. Springer, Berlin (2005)
Nielsen, J.: Participation Inequality: Encouraging More Users to Contribute. Jakob Nielsen’s Alertbox, http://www.useit.com/alertbox/participation_inequality.html
Nonnecke, B., Preece, J.: Silent participants: Getting to know lurkers better. In: Fisher, D., Lueg, C. (eds.) From Usenet to Co Webs: Interacting with social information spaces, pp. 110–132. Springer, Berlin (2003)
Pressley, L.: Using Social Software for Business Communication. Technical Report, Lauren Pressley (2006)
Prigogine, I.: Time, Structure and Fluctuations. Science 201(4358), 777–785 (1977)
Rheingold, H.: Smart Mobs: The Next Social Revolution. Perseus Books, New York (2003)
Russ, C.: Online Crowds-Extraordinary mass behavior on the Internet. In: Tochtermann, K., Maurer, H. (eds.) Proceedings of i-Media 2007, Graz, pp. 65–76 (2007)
Schelling, T.C.: Micromotives and Macrobehavior. W. W. Norton, New York (1978)
Watts, D.J.: The “New” Science of Networks. Annual Review of Sociology 30, 243–270 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Russ, C., Lux, M. (2008). A Model of Self-organized Criticality in Emergent Web Systems. In: Kaschek, R., Kop, C., Steinberger, C., Fliedl, G. (eds) Information Systems and e-Business Technologies. UNISCON 2008. Lecture Notes in Business Information Processing, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78942-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-78942-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78941-3
Online ISBN: 978-3-540-78942-0
eBook Packages: Computer ScienceComputer Science (R0)