ealizable. But the inherent complexity of supply chains makes the efficient utilization of information technology an elusive endeavor. Tackling this complexity has been beyond the existing tools <strong>and</strong> techniques <strong>and</strong> requires revival <strong>and</strong> extensions. As a result we emphasized in this paper, that in order to effectively underst<strong>and</strong> a supply chain network, it should be treated as a CAS. We laid down some initial ideas for the extension of modeling <strong>and</strong> analysis of supply chains using the concepts, tools <strong>and</strong> techniques arising in the study of CAS. As a future work we need to verify the feasibility <strong>and</strong> usefulness of the proposed techniques in the context of large scale supply chains. Acknowledgements The authors wish to acknowledge <strong>DARPA</strong> (Grant#: MDA972-1-1-0038 under UltraLog Program) for their generous support for this research. In additions the partial support provided by NSF (Grant#:DMII-0075584) for Professor Kumara is greatly appreciated. References Abarbanel, H.D.I, 1996, The Analysis of Observed Chaotic Data, Springer-Verlag, New York. Abarbanel, H. D. I. <strong>and</strong> Kennel, M. B., 1993, Local False Nearest Neighbors <strong>and</strong> Dynamical Dimensions from Observed Chaotic Data, Phys. Rev. E, 47, 3057-3068. Adami, C., 1998, Introduction to Artificial Life, Springer-Verlag. Albert, R. <strong>and</strong> Barabasi, A. L., 2002, Statistical Mechanics of Complex Networks, Reviews of Modern Physics, 74, 47. Albert, R., Barabási, A. L., Jeong, H. <strong>and</strong> Bianconi, G., 2000, Power-law distribution of the World Wide Web, Science, 287, 2115. Albert R., Jeong, H., Barabasi, A. L.,2000, Error <strong>and</strong> attack tolerance of complex networks, Nature, 406, 378-382. Balakrishnan, A., Kumara, S. <strong>and</strong> Sundaresan, S., 1999, Exploiting Information Technologies for Product Realization, Information Systems Frontiers, A Journal of Research <strong>and</strong> Innovation, 1(1), 25-50. Barabasi, A.L., July 2000, The Physics of Web, Physics Web. Barabasi, A. L., Albert, R., <strong>and</strong> Jeong, H., 2000, Scale-free characteristics of r<strong>and</strong>om networks: The topology of the World Wide Web, Physica A, 281, 69-77. Baranger, M., Chaos, Complexity, <strong>and</strong> Entropy: A physics talk for non-physicists, http://necsi.org/projects/baranger/cce.pdf. Bar-Yam, Y., 1997, Dynamics of complex systems, Reading, Mass, Addison-Wesley. Bollobas, B., 1985, R<strong>and</strong>om Graphs, Academic Press, London. Callaway, D. S., Newman, M. E. J., Strogatz, S. H. <strong>and</strong> Watts, D. J., 2000, Network robustness <strong>and</strong> fragility: Percolation on r<strong>and</strong>om graphs, Phys. Rev. Lett. 85, 5468-5471. Carlson, J. M., Doyle, J., 1999, Highly optimized tolerance: a mechanism for power laws in designed systems, Physics Review E, 60(2), 1412-1427. Casdalgi, M., 1989, Nonlinear prediction of chaotic time series, Physica D, 35, 335-356. Choi, T. Y., Dooley, K. J., Ruangtusanathan, M., 2001, Supply networks <strong>and</strong> complex adaptive systems: control versus emergence, Journal of Operations Management 19(3), 351-366. Cooper, M. C., Lambert, D. M., <strong>and</strong> Pagh, J. D., 1997, Supply chain management: More than a new name for logistics, The International Journal of Logistics Management, 8(1), 1-13. Crutchfield, J. P., 1992, Knowledge <strong>and</strong> Meaning … Chaos <strong>and</strong> Complexity, in Modeling Complex Systems, L. Lam <strong>and</strong> H. C. Morris, editors, Springer-Verlag, Berlin, 66 -101. Crutchfield, J. P., 1994, The Calculi of Emergence: Computation, Dynamics <strong>and</strong> Induction, Physica D, 75, 11-54. Crutchfield, J. P. <strong>and</strong> Young, K., 1989, Inferring Statistical Complexity, Physical Review Letters, 63, 105-108.
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
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Executive Summary Ultra*Log is a De
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2.3 Gnanasambandam, N., Lee, S., Ku
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6 Characterization and analysis of
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timizing simultaneously the link de
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where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
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Table 2. GA Results Agent N A D max
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connected component, in which a pat
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Random Small-world Scale-free with
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Growth mechanisms Start with a smal
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Table 2. The proposed network’s c
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tems. Proceedings of the Second Joi
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Proceedings of the 1st Open Cougaar
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1 SITUATION IDENTIFICATION USING DY
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3 3.2 Behavior In SSC society an ag
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5 All the behavior parameters may n
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Estimating Global Stress Environmen
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chaotic deterministic time series.
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100% 1 95% 2 14 15 64% TAO 4 64% 62
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interactions as a dynamical system
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ehavior states under varying system
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Extensive testing and validation of
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5 Conclusions and Future Research T
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2 to function critically even under
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architecture based on both the Grid
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limit of large number of tasks. If
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