Agent-Based Simulation for Complex Social Systems: Support for the Developer

Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

The successful implementation of policies in complex social environments, require a deep understanding of interdependencies between many actors with different perspectives. In order to understand, analyse and design such complex systems, advanced modelling tools are required. In this paper, we describe the MAIA modeling tool, based on the IAD framework. MAIA supports the development of agent-based models for policy making by providing (1) a methodology that provides guidelines on how to produce executable code from a conceptualized model, (2) a web-based application that supports the conceptualization process, and (3) a (semi) automatic transformation to generate executable simulations.

References

  1. 1.
    Drogoul A, Vanbergue D, Meurisse T (2003) Multi-agent based simulation: where are the agents? In: Multi-agent-based simulation II, pp 43–49 Google Scholar
  2. 2.
    Ghorbani A, Dignum V, Sheoratan S, Dijkema G (2011) Applying the Maia methodology to model the informal E-waste recycling sector. In: The seventh conference of the European social simulation association (ESSA) Google Scholar
  3. 3.
    Gilbert GN, Troitzsch KG (2005) Simulation for the social scientist. Open University Press, Milton Keynes. ISBN 0335216005 Google Scholar
  4. 4.
    Hassan S, Fuentes-Fernández R, Galán JM, López-Paredes A, Pavón J (2009) Reducing the modeling gap: On the use of metamodels in agent-based simulation. In: 6th conference of the European social simulation association (ESSA 2009), pp 1–13 Google Scholar
  5. 5.
    Heath B, Hill R, Ciarallo F (1998) A survey of agent-based modeling practices (January 1998 to July 2008). Journal of Artificial Societies and Social Simulation 12(4):9 Google Scholar
  6. 6.
    Janssen MA, Alessa LN, Barton M, Bergin S, Lee A (2008) Towards a community framework for agent-based modelling. Journal of Artificial Societies and Social Simulation 11(2):6 Google Scholar
  7. 7.
    Ligtvoet A, Ghorbani A, Chappin EJ (2011) A methodology for agent-based modeling using institutional analysis applied to consumer lighting. In: Agent technologies for energy systems, Tenth international conference on autonomous agents and multi agent systems (AAMAS), Taipei, Taiwan Google Scholar
  8. 8.
    Minar N (1996) The swarm simulation system: a toolkit for building multi-agent simulations. Santa Fe Institute, Santa Fe Google Scholar
  9. 9.
    North MJ, Collier NT, Vos JR (2006) Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation (TOMACS) 16(1):1–25 CrossRefGoogle Scholar
  10. 10.
    Ostrom E (2005) Understanding institutional diversity. Princeton University Press, Princeton Google Scholar
  11. 11.
    Ostrom TM (1988) Computer simulation: the third symbol system. Journal of Experimental Social Psychology 24(5):381–392 CrossRefGoogle Scholar
  12. 12.
    Sansores C, Pavón J (2005) Agent-based simulation replication: a model driven architecture approach. In: MICAI 2005: Advances in artificial intelligence, pp 244–253 CrossRefGoogle Scholar
  13. 13.
    Scharpf FW (1997) Games real actors play: actor-centered institutionalism in policy research. Westview Press, Boulder Google Scholar
  14. 14.
    Steubing B, Kostadinov F, Ghorbani A, Wager P, Zaha R, Thees C, Ludwig C (2011) Agent-based modeling of a woodfuel market factors that affect the availability of woodfuel. In: ISIE’11, Berkeley, US Google Scholar
  15. 15.
    Tisue S (2004) Netlogo: design and implementation of a multi-agent modeling environment. In: Proceedings of agent Google Scholar
  16. 16.
    Winograd T, Bennett J, De Young L, Hartfield B (1996) Bringing design to software. ACM Press, New York Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Faculty of Technology, Policy and ManagementDelft University of TechnologyDelftThe Netherlands

Personalised recommendations