Foundations of and Recent Advances in Artificial Life Modeling with Repast 3 and Repast Simphony

  • Michael J. North
  • Charles M. Macal

Artificial life focuses on synthesizing “life-like behaviors from scratch in computers, machines, molecules, and other alternative media” [24]. Artificial life expands the “horizons of empirical research in biology beyond the territory currently circumscribed by life-as-we-know-it” to provide “access to the domain of life-as-it-could-be” [24]. Agent-based modeling and simulation (ABMS) are used to create computational laboratories that replicate real or potential behaviors of actual or possible complex adaptive systems (CAS). The goal of agent modeling is to allow experimentation with simulated complex systems. To achieve this, agent-based modeling uses sets of agents and frameworks for simulating the agent's decisions and interactions. Agent models show how complex adaptive systems may evolve through time in a way that is difficult to predict from knowledge of the behaviors of the individual agents alone. Agent-based modeling thus provides a natural framework in which to perform artificial life experiments. The free and open source Recursive Porous Agent Simulation Toolkit (Repast) family of tools consists of several advanced agent-based modeling toolkits.


Supply Chain Geographical Information System Killer Whale Complex Adaptive System Runtime System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Al-Mutawah K and Lee V (2008) An Evaluation Framework for Supply Chains Based on Corporate Culture Compatibility. In: Supply Chain, Theory and Applications, Ko-rdic V (ed.) pp. 59–72, I-Tech Education and Publishing, Vienna, Austria.Google Scholar
  2. 2.
    Archer T (2001) Inside C#. Microsoft Press, Redmond, Washington.Google Scholar
  3. 3.
    Beck K and Gamma E (1998) Test infected: Programmers love writing tests. Java Report 3:37–50.Google Scholar
  4. 4.
    de Bie P and de Boer B (2007) An Agent-Based Model of Linguistic Diversity. In: Proc. ESSLLI 2007 Workshop on Language, Games, and Evolution, Benz A, Ebert C and van Rooij R (eds.), pp. 1–8, Available online at
  5. 5.
    Booch G (1993) Object-oriented Design with Applications. Addison-Wesley, Reading, MA.Google Scholar
  6. 6.
    Carpenter, C., 2004, Agent-Based Modeling of Seasonal Population Movement and the Spread of the 1918–1919 Flu: The Effect on a Small Community, University of Missouri-Columbia, Master's Thesis, Department of Anthropology.Google Scholar
  7. 7.
    Charania AC, Olds JR, and DePasquale D (2006) Sub-Orbital Space Tourism Market: Predictions of the Future Marketplace Using Agent-Based Modeling, Space-Works Engineering, Inc., Atlanta, GA, Available online at uploads/archive/IAC-06-E3.4.pdf.
  8. 8.
    Cloyer A, Clement A, Bodkin R, and Hugunin J (2003) Practitioners report: Using aspectJ for component integration in middleware. In: Companion of the 18th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications. R. Crocker and G. Steele Jr. (eds.) ACM, New York.Google Scholar
  9. 9.
    Collier N, Howe T, and North M (2003) Onward and upward: The transition to Repast 2.0. In: Proc. of the 1st Annual North American Association for Computational Social and Organizational Science Conference, Electronic Proceedings. Pittsburgh, PA.Google Scholar
  10. 10.
    Conway SR (2006) An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility, ICAS 2006 – 25th Congress of the International Council of the Aeronautical Sciences Hamburg, Germany, 3–8 September 2006. Available online at gov/20060048296_2006250468.pdf.
  11. 11.
    van Dam KH, Lukszo Z, Ferreira L, and Sirikijpanichkul A (2007) Planning the Location of Intermodal Freight Hubs: An Agent Based Approach, In: Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, pp. 187–192, London, UK, 15–17 April 2007.Google Scholar
  12. 12.
    Di Paolo E (2004) Unbinding biological autonomy: Francisco Varela's contributions to artificial Life. Journal of Artificial Life, Vol. 10, Issue 3, 231–234.CrossRefGoogle Scholar
  13. 13.
    Eclipse Home Page (2008)
  14. 14.
    Elrad T, Filman R, and Bader A (2001) Aspect-oriented programming: Introduction. Communications of ACM 44:29–32.CrossRefGoogle Scholar
  15. 15.
    Foxwell H (1999) Java 2 Software Development Kit. Linux Journal. Specialized Systems Consultants, Seattle, Washington.Google Scholar
  16. 16.
    Gamma E, Helm R, Johnson R, and Vlissides J (1995) Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Reading, MA.Google Scholar
  17. 17.
    Griffin AF and Stanish C (2007) An agent-based model of prehistoric settlement patterns and political consolidation in the Lake Titicaca Basin of Peru and Bolivia, structure and dynamics: eJournal of Anthropological and Related Sciences, 2(2). Available online at vol2/iss2/art2.
  18. 18.
    Gülcü C (2003) The Complete Log4j Manual: The Reliable, Fast, and Flexible Logging Framework for Java., Lausanne, SwitzerlandGoogle Scholar
  19. 19.
    Holland J (1996) Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, Reading, MA.Google Scholar
  20. 20.
    Houwing M and Bouwmans I (2007) Agent-Based Modelling of Residential Energy Generation with Micro-CHP, Delft University of Technology, Available online at vol2/iss2/art2.
  21. 21.
    Howe TR, Collier NT, North MJ, Parker BT, and Vos JR (2006) Containing Agents: Contexts, Projections, and Agents. In: Proceedings of the Agent 2006 Conference on Social Agents: Results and Prospects, Argonne National Laboratory, Argonne, IL.Google Scholar
  22. 22.
    Java Object Oriented Neural Engine (Joone) Home Page (2004) http://www.
  23. 23.
    König D, Glover A, King P, Laforge G, and Skeet J (2007) Groovy in Action. Manning Publications, Greenwich, CT.Google Scholar
  24. 24.
    Langton C (1994) What is Artificial Life?, The Digital Biology Project. Available at
  25. 25.
    Law AA (2007) Simulation Modeling and Analysis. 4th ed. McGraw-Hill, New York.Google Scholar
  26. 26.
    López-Sánchez M, Noria X, Rodriguez JA, and Gilbert N (2005) Multi-agent based simulation of news digital markets. International Journal of Computer Science & Applications, II(I). Available online at
  27. 27.
    Lutz M and Ascher D (1999) Learning Python. O'Reilly Press, Sebastopol, CA.zbMATHGoogle Scholar
  28. 28.
    Mast EH, van Kuik GAM, and van Bussel GJW (2007) Agent-Based Modelling for Scenario Development of Offshore Wind Energy, T. Chaviaropoulos (ed.), Proceedings of the 2007 European Wind Energy Conference & Exhibition in Milan, pp. 1–4, Brussels, EWEA.Google Scholar
  29. 29.
    Mock KJ and Testa JW (2007) An Agent-Based Model of Predator—Prey Relationships between Transient Killer Whales and Other Marine Mammals, University of Alaska Anchorage, Anchorage, AK, May 31, 2007. Available online at http://www.math.
  30. 30.
    Mozart Consortium: Mozart Programming System 1.3.1 (2004). Available online at
  31. 31.
    Narzisi GV, Mishra B (2006) Multi-Objective Evolutionary Optimization of Agent-Based Models: An Application to Emergency Response Planning, New York University, Available online at
  32. 32.
    NCSA, HDF 5 Home Page (2004)
  33. 33.
    North M, Collier N, and Vos R (2006) Experiences creating three implementations of the Repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation, 16(1):1–25.CrossRefGoogle Scholar
  34. 34.
    North M and Macal C (2007) Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford University Press, New York.Google Scholar
  35. 35.
    North MJ and Macal CM (2005) Escaping the Accidents of History: An Overview of Artificial Life Modeling with Repast, In: Adamatzky A and Komosinski M (eds.). Artificial Life Models in Software, 1st ed., pp. 115–141, Springer, Heidelberg.Google Scholar
  36. 36.
    North MJ, Tatara E, Collier NT, Ozik J (2007) Visual Agent-based Model Development with Repast Simphony. In: Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence, Argonne National Laboratory, Argonne, IL.Google Scholar
  37. 37.
    Parry H, Evans AJ and Morgan D (2004) Aphid Population Dynamics in Agricultural Landscapes: An Agent-Based Simulation Model, International Environmental Modelling and Software Society iEMSs 2004 International Conference University of Osnabrück, Germany, 14–17 June 2004. Available online at http://www.iemss. org/iemss2004/pdf/landscape/parraphi.pdf.
  38. 38.
    ROAD: Repast 3.0 (2004)
  39. 39.
    Sandler T (2001) Economic Concepts for the Social Sciences. Cambridge University Press, Cambridge.Google Scholar
  40. 40.
    Swarm Development Group: Swarm 2.2 (2004)
  41. 41.
    Tonmukayakul A (2007) An Agent-Based Model for Secondary Use of Radio Spectrum. Ph.D. thesis, University of Pittsburgh, School of Information Sciences.Google Scholar
  42. 42.
    Van Roy P and Haridi S (2004) Concepts, Techniques, and Models of Computer Programming. MIT Press, Boston, MA.Google Scholar
  43. 43.
    Walker R, Baniassad E, and Murphy G (1999) An initial assessment of aspect-oriented programming. In: Proc. 1999 Int. Conf. Software Engineering. IEEE, Piscataway, NJ, pp. 120–135.CrossRefGoogle Scholar
  44. 44.
    Wragg T (2006) Modelling the Effects of Information Campaigns Using Agent-Based Simulation, DSTO Defence Science and Technology Organisation, Edinburgh South Australia, DSTO-TR-1853.Google Scholar
  45. 45.
    Yin L (2007) Assessing indirect spatial effects of mountain tourism development: An application of agent-based spatial modeling. Journal of Regional Analysis & Policy 37(3):257–265. Available online at pastvolumes/2000/v37/F37-3-8.pdf.Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Michael J. North
    • 1
  • Charles M. Macal
    • 1
  1. 1.Argonne National LaboratoryArgonneUSA

Personalised recommendations