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Computational Intelligence: A Compendium pp 409–437Cite as

Open Source Agent-Based Modeling Frameworks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 115))

Artificial Life as a field of study was inaugurated by Chris Langton, who described it as the study of man-made systems exhibiting behaviors characteristic of life. As such it is complementary to traditional biology, locating ‘life-as-we-know-it’ within the larger picture of ‘life-as-it-could-be’ [17].

This Chapter surveys open source (see Sect. 3), agent-based modeling platforms. Being open source is important, for ensuring replicability of results between different research groups, and also for auditing against implementation artifacts. This chapter does not examine commercial agent-based modeling options.

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References

  1. Adami C (1998) Introduction to Artificial Life. Springer-Verlag, Berlin.

    MATH  Google Scholar 

  2. Arthur WB, Holland JH, LeBaron B, Palmer R, Taylor P (1996) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur WB, Durlauf S, Lane DA (eds.) The Economy as an Evolving, Complex System II. Addison-Wesley, Menlo Park, CA: 15-44.

    Google Scholar 

  3. Bedau MA (2002) Downward causation and the autonomy of weak emergence. Principia, 6: 5-50.

    Google Scholar 

  4. Boisvert F, Moreira J, Philippsen M, Pozo R (2001) Java and numerical com-puting. Computing in Science & Engineering (see also IEEE Computational Science and Engineering), 32: 18-24.

    Google Scholar 

  5. Bousquet RF, Bakam I, Proton H, Le Page C (1998) Cormas: common-pool resources and multi-agent systems. In: del Pobil AP, Mira J, Ali M (eds.) Tasks and Methods in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence 1416, Springer-Verlag, Berlin: 826-838.

    Chapter  Google Scholar 

  6. Colella VS, Klopfer E, Resnick M (2001) Adventures in Modeling. Teachers College Press, New York, NY.

    Google Scholar 

  7. Epstein JM, Axtell RL (1996) Growing Artificial Societies: Social Science From the Bottom Up. MIT Press, Cambridge, MA.

    Google Scholar 

  8. Free Software Foundation. The free software definition. (available online at http://www.fsf.org/licensing/essays/free-sw.html - last accessed April 2007).

  9. Franklin S, Graesser A (1997) Is it an agent, or just a program?: A taxonomy for autonomous agents. In: Müller JP, Wooldridge MJ, Jennings NR (eds.) Intelligent Agents III Agent Theories, Architectures, and Languages, Lecture Notes in Computer Science 1193, Springer-Verlag, Berlin: 21-35.

    Chapter  Google Scholar 

  10. Fromm J (2004) The Emergence of Complexity. Kassel University Press, Germany.

    Google Scholar 

  11. Galassi M, Davies J, Theiler J, Gough B, Jungman G, Booth M, Rossi F (2005) GNU Scientific Library Reference Manual (revised 2nd ed.). Network Theory Ltd, Bristol, UK.

    Google Scholar 

  12. Grimm V (1999) Ten years of individual based modeling in ecology: what have we learned and what could we learn in the future? Ecological modeling, 115: 129-148.

    Article  Google Scholar 

  13. Holland JH (1997) Emergence: From Chaos to Order. Addison Wesley, Reading, MA.

    Google Scholar 

  14. Hörmann W, Leydold J, Derflinger G (2004) Automatic Nonuniform Random Variate Generation (Statistics and Computing Series). Springer-Verlag, Berlin.

    MATH  Google Scholar 

  15. Huston M, DeAngelis E, Post W (1988) New computer models unify ecological theory. Bioscience, 381: 682-691.

    Article  Google Scholar 

  16. Kahan W, Darcy JD How java’s floating-point hurts everyone. (available online at http://www.cs.berkeley.edu/˜wkahan/JAVAhurt.pdf - last accessed April 2007).

  17. Langton CG (1988) Artificial life. In: Langton C (ed.) Artificial Life. Addison-Wesley, Reading, MA: 1.

    Google Scholar 

  18. Lewis JP, Neumann U (2003) Performance of Java versus C++. (available online at http://www.idiom.com/˜zilla/Computer/javaCbenchmark.html - last accessed April 2007).

  19. Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: A multiagent simulation environment. Simulation, 81: 517-527.

    Article  Google Scholar 

  20. Madina D, Standish RK (2001) A system for reflection in C++. In: Proc. AUUG2001: Always on and Everywhere, 26-28 September, Sydney, Australian Unix Users Group Inc., Kensington, NSW: 207.

    Google Scholar 

  21. McMullin B (2004) Thirty years of computational autopoiesis: A review. Artificial Life, 10: 277-295.

    Article  Google Scholar 

  22. Minar N, Burkhart R, Langton CG, Askenazi M (1996) The Swarm simulation system: a toolkit for building multi-agent simulations. Technical Report WP96-06-042, Santa Fe Institute. (available online at http://www.swarm.org - last accessed April 2007).

  23. North MJ, Collier NT, Vos JR (2006) Experiences creating three implementations of the Repast agent modeling toolkit. ACM Trans. Modeling and Computer Simulation, 16: 1-25.

    Article  Google Scholar 

  24. Ousterhout JK (1994) TCL and the Tk Toolkit. Addison Wesley, Reading, MA.

    MATH  Google Scholar 

  25. Ousterhout JK (1998) Scripting: Higher-level programming for the 21st century. IEEE Computer, 313: 23-30.

    Google Scholar 

  26. Palmer RG, Arthur WB, Holland JH, LeBaron B, Tayler P (1994) Artificial economic life: a simple model of a stock market. Physica D, 75: 264-274.

    Article  MATH  Google Scholar 

  27. Parisi G, Rapuano F (1985) Effects of the random number generator on computer simulations. Physics Letters B, 157: 301-302.

    Article  MathSciNet  Google Scholar 

  28. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: Review and development recommendations. Simulation, 82: 609-623.

    Article  Google Scholar 

  29. Rathgeber HD (1963) Mousetrap model of chain reactions. American J. Physics, 31: 62.

    Google Scholar 

  30. Ray T (1991) An approach to the synthesis of life. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds.) Artificial Life II. Addison Wesley, Reading, MA: 371.

    Google Scholar 

  31. Standish RK (1994) Population models with random embryologies as a paradigm for evolution. Complexity International, 2.

    Google Scholar 

  32. Standish RK (2001) On complexity and emergence. Complexity International, 9.

    Google Scholar 

  33. Standish RK, Leow R (2003) EcoLab: Agent based modeling for C++ program-mers. In: Proc. SwarmFest 2003, 13-15 April, Notre Dame, IN (available online at http://www.nd.edu/∼swarm03/Program/program.html - last accessed April 2007).

  34. Standish RK, Madina D (2003) ClassdescMP: Easy MPI programming in C++. In: Sloot PMA, Abramson D, Bogdanov AV, Dongarra JJ, Zomaya AY, Gor-bachev YE (eds.), Computational Science, Lecture Notes in Computer Science 2660, Springer-Verlag, Berlin: 896.

    Google Scholar 

  35. Thearling K, Ray T (1994) Evolving multi-cellular artificial life. In: Brooks RA, Maes P (eds) Artificial Life IV, MIT Press, Cambridge, MA: 283-288.

    Google Scholar 

  36. Wuensche A (1999) Discrete dynamical networks and their attractor basins. Complexity International, 6.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, University of New South Wales, NSW, 2052, Australia

    Russell K. Standish

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  1. Russell K. Standish
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Editors and Affiliations

  1. School of Computer Science and Software Engineering Faculty of Informatics, University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia

    John Fulcher

  2. Knowledge-Based Engineering Founding Director of the KES Centre, University of South Australia, SCT-Building Mawson Lakes Campus, Adelaide, South Australia SA, 5095, Australia

    L. C. Jain

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Standish, R.K. (2008). Open Source Agent-Based Modeling Frameworks. In: Fulcher, J., Jain, L.C. (eds) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78293-3_10

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  • DOI: https://doi.org/10.1007/978-3-540-78293-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78292-6

  • Online ISBN: 978-3-540-78293-3

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