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Introduction to Modeling of Complex Systems Using Cellular Automata

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Simulating Complex Systems by Cellular Automata

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Since the sixteenth century there have been two main paradigms in the methodology of doing science. The first one is referred to as “the experimental” paradigm. During an experiment we observe, measure, and quantify natural phenomena in order to solve a specific problem, answer a question, or to decide whether a hypothesis is true or false. The second paradigm is known as “the theoretical” paradigm. A theory is generally understood as a fundamental, for instance logical and/or mathematical explanation of an observed natural phenomenon. Theory can be supported or falsified through experimentation.

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References

  1. P. Bak, How Nature Works: The Science of Self-Organized Criticality. (Springer, New York, NY, 1996)

    Book  Google Scholar 

  2. S. Bandini, B. Chopard, M. Tomassini (eds.), Cellular Automata, 5th International Conference on Cellular Automata for Research and Industry, ACRI 2002, Geneva, Switzerland, October 9–11, 2002, Proceedings Lecture Notes in Computer Science, vol. 2493 (Springer, Heidelberg, 2002)

    MATH  Google Scholar 

  3. L. Berec, Techniques of spatially explicit individual-based models: Construction, simulation and mean-field analysis. Ecol. Model. 150, 55–81 (2002)

    Article  Google Scholar 

  4. N. Boccara, Modeling Complex Systems (Springer, Heidelberg, 2004)

    MATH  Google Scholar 

  5. B. Chopard, M. Droz, Cellular Automata Modeling of Physical Systems, (Cambridge University Press, Cambridge, 2005)

    Google Scholar 

  6. K. Christensen, N. Moloney, Complexity and Criticality (Imperial College Press, London, 2005)

    Book  Google Scholar 

  7. A. Deutch, S. Dormann, Cellular Automaton Modeling of Biological Pattern Formation (Birkhauser, Basel, 2004)

    Google Scholar 

  8. M. Gardner, The fantastic combinations of John Conway’s new solitaire game “life”. Sci. Am. 223, 120–123 (1970)

    Article  Google Scholar 

  9. B. Hölldobler, E. Wilson, Journey to the Ants: A Story of Scientific Exploration, 3rd edn. (Harvard University Press, Cambridge, MA, 1995)

    Google Scholar 

  10. A. Ilachinski, Cellular Automata: A Discrete Universe (World Scientific Publishing Co. Pte. Ltd., London, 2001)

    Google Scholar 

  11. International Federation for Information Processing (IFIP), Working Group 1.5 on Cellular Automata and Machines, http://liinwww.ira.uka.de/ca/software/index.html: A list of software packages for Cellular Automata, http://liinwww.ira.uka.de/ca/software/index.html

  12. J. Kroc, Special issue on modelling of complex systems by cellular automata 2007: Guest editors’ introduction. Adv. Compl. Syst. 10(1 supp), 1–3 (2007)

    MathSciNet  Google Scholar 

  13. C. Langton, Computation at the edge of chaos: Phase transitions and emergent computation. Physica D 42, 12–27 (1990)

    Article  MathSciNet  Google Scholar 

  14. M. Mitchell, Nonstandard Computation, chap. Computation in cellular automata: a selected review (VCH, Weinheim Verlagsgesellschaft, 1998) pp. 95–140

    Google Scholar 

  15. A. Nathan, V. Barbosa, V-like formations in flocks of artificial birds. ArXiv Computer Science e-prints (2006), http://arxiv.org/abs/cs/0611032

  16. J. von Neumann, A. Burks, Theory of Self-Reproducing Automata (University of Illinois Press, Urbana, IL 1966)

    Google Scholar 

  17. M. Resnick, Turtles, Termites, and Traffic Jams – Explorations in Massively Parallel Microworlds (The MIT Press, Cambridge, MA, 1997)

    Google Scholar 

  18. M. Resnick, StarLogo – programmable environment for exploring decentralized systems flocks, traffic jams, termite and ant colonies. Tech. rep., MIT (2006), http://education.mit.edu/starlogo/

  19. P.M.A. Sloot, B.J. Overeinder, A. Schoneveld, Self organized criticality in simulated correlated systems. Comput. Phys. Comm. 142, 66–81 (2001)

    Article  Google Scholar 

  20. P. Sloot, B. Chopard, A. Hoekstra (eds.), Cellular Automata, 6th International Conference on Cellular Automata for Research and Industry, ACRI 2004, Amsterdam, The Netherlands, October 25–28, 2004, Proceedings. Lecture Notes in Computer Science, vol. 3305 (Springer, Heidelberg, 2004)

    MATH  Google Scholar 

  21. P.M.A. Sloot, A.G. Hoekstra, Modeling dynamic systems with cellular automata, ed. by P.A. Fishwick Handbook of Dynamic System Modelling chapter 21 (Chapman and Hall London, 2007)

    Google Scholar 

  22. T. Toffoli, Cellular automata as an alternative to (rather than an approximation of) differential equations in modelling physics. Physica D17, 117–127 (1984)

    MathSciNet  Google Scholar 

  23. T. Toffoli, N. Margolus, Cellular Automata Machines: A New Environment for Modeling (MIT Press, Cambridge, MA 1987)

    Google Scholar 

  24. A.M. Turing, The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma (Oxford University Press, New York, NY, 2004)

    Google Scholar 

  25. G. Vichniac, Simulating physics with cellular automata. Physica D 17, 96–116 (1984)

    Article  MathSciNet  Google Scholar 

  26. S. Wolfram, Universality and complexity in cellular automata. Physica D 1–35 (1984)

    Google Scholar 

  27. S. Wolfram, A New Kind of Science (Wolfram Media Inc., Champaign, II 2002)

    MATH  Google Scholar 

  28. S.E. Yacoubi, B. Chopard, S. Bandini, (eds.) Cellular Automata, 7th International Conference on Cellular Automata, for Research and Industry, ACRI 2006, Perpignan, France, September 20–23, 2006, Proceedings Lecture Notes in Computer Science, vol. 4173 (Springer, Heidelberg, 2006)

    MATH  Google Scholar 

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Correspondence to Alfons G. Hoekstra .

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Hoekstra, A.G., Kroc, J., Sloot, P.M. (2010). Introduction to Modeling of Complex Systems Using Cellular Automata. In: Kroc, J., Sloot, P., Hoekstra, A. (eds) Simulating Complex Systems by Cellular Automata. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12203-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-12203-3_1

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  • Print ISBN: 978-3-642-12202-6

  • Online ISBN: 978-3-642-12203-3

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