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Post-mortem analysis of emergent behavior in complex simulation models

Published:19 May 2013Publication History

ABSTRACT

Analyzing and validating emergent behavior in component-based models is increasingly challenging as models grow in size and complexity. Despite increasing research interest, there is a lack of automated, formalized approaches to identify emergent behavior and its causes. As part of our integrated framework for understanding emergent behavior, we propose a post-mortem emergence analysis approach that identifies the causes of emergent behavior in terms of properties of the composed model and properties of the individual model components, and their interactions. In this paper, we detail the use of reconstructability analysis for post-mortem analysis of known emergent behavior. The two-step process first identifies model components that are most likely to have caused emergent behavior, and then analyzes their interaction. Our case study using small and large examples demonstrates the applicability of our approach.

References

  1. M. Bedau. Weak Emergence. Philosophical Perspectives, 11:375--399, 1997.Google ScholarGoogle Scholar
  2. R. Cavallo and G. Klir. Reconstructability Analysis of Multi-dimensional Relations: A Theoretical Basis for Computer-aided Determination of Acceptable System Models. Int. Journal of General Systems, 5:143--171, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  3. W. Chan, Y. S. Son, and C. M. Macal. Simulation of Emergent Behavior and Differences Between Agent-Based Simulation and Discrete-Event Simulation. In Proceedings of the Winter Simulation Conference, pages 135--150, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. W. K. V. Chan. Interaction Metric of Emergent Behaviors in Agent-based Simulation. In Proceedings of the Winter Simulation Conference, pages 357--368, Phoenix, USA, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Chen, S. B. Nagl, and C. D. Clack. Specifying, Detecting and Analysing Emergent Behaviours in Multi-Level Agent-Based Simulations. In Proceedings of the Summer Computer Simulation Conference, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L. Chi. Transplating Social Capital to the Online World: Insights from Two Experimental Studies. Journal of Organizational Computing and Electronic Commerce, 19:214--236, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  7. P. Cilliers. Complexity & Postmodernism. Routledge, 1998.Google ScholarGoogle Scholar
  8. P. Davis. New Paradigms and Challenges. In Proceedings of the Winter Simulation Conference, Orlando , USA, 2005.Google ScholarGoogle Scholar
  9. U. Fayyad and R. Uthurusamy. Evolving Data Into Mining Solutions for Insights. Communications of the ACM, 45, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Floyd and V. Jacobson. The synchronization of Periodic Routing Messages. In Proceedings of SIGCOMM, pages 33--44, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Gardner. The Fantastic Combinations of John Conway's New Solitaire Games. Mathematical Games, 1970.Google ScholarGoogle Scholar
  12. C. Gershenson and N. Fernandez. Complexity and Information: Measuring Emergence, Self-organization, and Homeostatis at Multiple Scales. Complexity, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Gore and P. Reynolds. An Exploration-based Taxonomy for Emergent Behavior Analysis in Simulation. In Proceedings of the Winter Simulation Conference, pages 1232--1240, Miami, USA, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. R. Gore and P. Reynolds. Applying Causal Inference to Understand Emergent Behavior. In Proceedings of the Winter Simulation Conference, pages 712--721, Miami, USA, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Holland. Emergence, From Chaos to Order. Basic Books, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. O. T. Holland. Taxonomy for the Modeling and Simulation of Emergent Behavior Systems. In Proceedings of the 2007 Spring Simulation Multiconference, pages 28--35, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. W. Johnson. What are Emergent Properties and How Do They Affect the Engineering of Complex Systems? Reliability Engineering and System Safety, 12:1475--1481, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  18. A. Kubik. Towards a Formalization of Emergence. Journal of Artificial Life, 9:41--65, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. Manley and T. Cheng. Understanding Road Congestion as an Emergent Property of Traffic Networks. In Proceedings of International Multiconference on Complexity, Informatics and Cybernetics, pages 109--114, 2010.Google ScholarGoogle Scholar
  20. J. C. Mogul. Emergent (mis)behavior vs. Complex Software Systems. In Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems, pages 293--304, New York, USA, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Occam. Occam reconstructability analysis: http://dmm.sysc.pdx.edu/, Last retrieved Feb. 2013.Google ScholarGoogle Scholar
  22. E. Page and J. Opper. Observations on the Complexity of Composable Simulations. In Proceedings of the Winter Simulation Conference, volume 1, pages 553--560, Phoenix, USA, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Prokopenko, F. Boschetti, and A. J. Ryan. An Information-theoretic Primer of Complexity, Self-organization and Emergence. Complexity, 15:11--28, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. K. K. Ramakrishnan and H. Yang. The Ethernet Capture Effect: Analysis and Solution. In Proceedings of the IEEE Local Computer Networks Conference, Minneapolis, USA, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  25. C. Reynolds. Flocks, Herds, and Schools: A Distributed Behavioral Model. In Proceedings of ACM SIGGRAPH, pages 25--34, 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. K. Seth. Measuring Emergence via Nonlinear Granger Causality. In Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, pages 545--553, 2008.Google ScholarGoogle Scholar
  27. C. Szabo, Y. Teo, and S. See. An Integrated Approach for the Validation of Emergence in Component-based Simulation Models. In Proceedings of the Winter Simulation Conference, pages 2412--2423, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. C. Szabo and Y. M. Teo. Semantic Validation of Emergent Properties in Component-based Simulation Models. Ontology, Epistemology, and Teleology of Modeling and Simulation -- Philosophical Foundations for Intelligent M&S Applications, pages 319--333, 2012.Google ScholarGoogle Scholar
  29. Y. Teo and C. Szabo. CODES: An Integrated Approach to Composable Modeling and Simulation. In Proceedings of the 41st Annual Simulation Symposium, pages 103--110, Ottawa, Canada, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. Treiber and A. Kesting. Traffic Flow Dynamics. Springer, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  31. B.-Y. Yaneer. A Mathematical Theory of Strong Emergence using Multiscale Variety. Complexity, 9:15--24, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. M. Zwick. An Overview of Reconstructability Analysis. Kybernetes, 33:877--905, 2004.Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Conferences
          SIGSIM PADS '13: Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
          May 2013
          426 pages
          ISBN:9781450319201
          DOI:10.1145/2486092

          Copyright © 2013 ACM

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          Publication History

          • Published: 19 May 2013

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          SIGSIM PADS '13 Paper Acceptance Rate29of75submissions,39%Overall Acceptance Rate398of779submissions,51%

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