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Tutorial on agent-based modeling and simulation

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Agent-Based Modeling and Simulation

Part of the book series: The OR Essentials series ((ORESS))

Abstract

Agent-based modeling and simulation (ABMS) is a relatively new approach to modeling systems composed of autonomous, interacting agents. Agent-based modeling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modeling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

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References

  • Alber MS, Kiskowski MA, Glazier JA and Jiang Y (2003). On cellular automaton approaches to modeling biological cells. In: Rosenthal J and Gilliam DS (eds). Mathematical Systems Theory in Biology, Communication, and Finance, IMA Vol. 134, Springer: New York, pp 1–39.

    Chapter  Google Scholar 

  • Alpaydin E (2004). Introduction to Machine Learning. MIT Press: Cambridge, MA.

    Google Scholar 

  • Arthur WB, Durlauf SN and Lane DA (eds). (1997). The Economy as an Evolving Complex System II, SFI Studies in the Sciences of Complexity. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Axelrod R (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press: Princeton, NJ.

    Google Scholar 

  • Axtell R (2000). Why agents? On the varied motivations for agent computing in the social sciences. Working Paper 17, Center on Social and Economic Dynamics, Brookings Institution, Washington, DC.

    Google Scholar 

  • Azzedine B et al (2007). An agent-based and biological inspired real-time intrusion detection and security model for computer network operations. Comp Commun 30(13): 2649–2660.

    Article  Google Scholar 

  • Bagni R, Berchi R and Cariello P (2002). A comparison of simulation models applied to epidemics. J Artif Soc Social Simul 5(3), http://jasss.soc.surrey.ac.uk/5/3/5.html accessed 30 June 2002.

  • Bishop CM (2007). Pattern Recognition and Machine Learning. Springer: New York.

    Google Scholar 

  • Bonabeau E (2001). Agent-based modeling: Methods and techniques for simulating human systems. Proc Natl Acad Sci 99(3): 7280–7287.

    Google Scholar 

  • Carley KM et al (2006). Biowar: Scalable agent-based model of bioattacks. IEEE Trans Syst Man Cybernet 36(2): 252–265.

    Article  Google Scholar 

  • Casti J (1997). Would-Be Worlds: How Simulation is Changing the World of Science. Wiley: New York.

    Google Scholar 

  • Charania AC, Olds JR and DePasquale D (2006). Sub-orbital Space Tourism Market: Predictions of the Future Marketplace Using Agent-based Modeling. SpaceWorks Engineering, Inc.: Atlanta, GA, http://www.sei.aero/uploads/archive/IAC-06-E3.4.pdf.

    Google Scholar 

  • Cirillo R et al (2006). Evaluating the potential impact of transmission constraints on the operation of a competitive electricity market in illinois. Argonne National Laboratory, Argonne, IL, ANL-06/16 (report prepared for the Illinois Commerce Commission), April.

    Book  Google Scholar 

  • Eclipse Foundation (2009). Eclipse home page. http://www.eclipse.org/.

    Google Scholar 

  • Emonet T et al (2005). AgentCell: A digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11): 2714–2721.

    Article  Google Scholar 

  • Epstein JM (2002). Modeling civil violence: An agent-based computational approach. Proc Natl Acad Sci 99(90003): 7243–7250.

    Article  Google Scholar 

  • Epstein JM and Axtell R (1996). Growing Artificial Societies: Social Science from the Bottom Up. MIT Press: Cambridge, MA.

    Google Scholar 

  • Epstein JM et al (2007). Controlling pandemic flu: The value of international air travel restrictions. PLoS ONE 2(5): e401. doi:10.1371/journal.pone.0000401

    Article  Google Scholar 

  • Folcik VA, An GC and Orosz CG (2007). The basic immune simulator: An agent-based model to study the interactions between innate and adaptive immunity. Theoret Biol Med Model 4(39), http://www.tbiomed.com/content/4/1/39.

  • Gardner M (1970). The fantastic combinations of john conway’s new solitaire game “Life”. Scient Amer 223: 120–123.

    Article  Google Scholar 

  • Gilbert N and Troitzsch KG (2005). Simulation for the Social Scientist, 2nd edn, Open University Press: Maidenhead, UK.

    Google Scholar 

  • GMU (George Mason University) (2009). MASON home page. http://cs.gmu.edu/~eclab/projects/mason/.

    Google Scholar 

  • Goldberg DE (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Grimm V et al (2006). A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1–2): 115–126.

    Article  Google Scholar 

  • Hill RR, Carl RG and Champagne LE (2006). Using agent simulation models to examine and investigate search theory against a historical case study. J Simul 1(1): 29–38.

    Article  Google Scholar 

  • Holland J (1995). Hidden Order: How Adaptation Builds Complexity. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Holland JH et al (2000). What is a learning classifier system? In: Lanzi PL, Stolzmann W and Wilson SW (eds). Learning Classifier Systems, from Foundations to Applications. Springer-Verlag: London, UK, pp 3–32.

    Chapter  Google Scholar 

  • Jennings NR (2000). On agent-based software engineering. Artif Intell 117: 277–296.

    Article  Google Scholar 

  • Kauffman SA (1993). The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press: Oxford, UK.

    Google Scholar 

  • Kohler TA, Gumerman GJ and Reynolds RG (2005). Simulating ancient societies. Scient Amer 293(1): 77–84.

    Article  Google Scholar 

  • Langton CG (1989). Artificial life. In: Langton CG (ed). Artificial Life: The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems (held September 1987, Los Alamos, New Mexico, Vol. VI in Santa Fe Institute Studies in the Sciences of Complexity), Addison-Wesley: Reading, MA, pp 1–47.

    Google Scholar 

  • López-Sánchez M, Noria X, Rodríguez JA and Gilbert N (2005). Multi-agent based simulation of news digital markets. Int J Comp Sci Appl II(I). http://www.tmrfindia.org/ijcsa/v21.html.

  • Macal CM (2004a). Emergent structures from trust relationships in supply chains. In: Macal C, Sallach D and North M (eds). Proceedings of Agent 2004: Conference on Social Dynamics: Interaction, Reflexivity and Emergence. Argonne National Laboratory: Chicago, IL, 7–9 October, pp 743–760.

    Google Scholar 

  • Macal CM (2004b). Agent-based modeling and social simulation with Mathematica and MATLAB. In: Macal C, Sallach D and North M (eds). Proceedings of Agent 2004: Conference on Social Dynamics: Interaction, Reflexivity and Emergence. Chicago, IL, 7–9 October. http://www.agent2004.anl.gov/pp 185–204.

    Google Scholar 

  • Macal CM (2009). Agent based modeling and artificial life. In: Meyers R (ed). Encyclopedia of Complexity and Systems Science. Springer: New York, pp 112–131 (ISBN 978–0-387–75888-6).

    Chapter  Google Scholar 

  • Macy MW and Willer R (2002). From factors to actors: Computational sociology and agent-based modeling. Ann Rev Sociol 28: 143–166.

    Article  Google Scholar 

  • Marsh WE and Hill RR (2008). An initial agent behavior modeling and definition methodology as applied to unmanned aerial vehicle simulation. Int J Simul Process Model 4(2): 119–129.

    Article  Google Scholar 

  • Minar N, Burkhart R, Langton C and Askenazi M (1996). The swarm simulation system, a toolkit for building multi-agent simulations. Working Paper 96–06-042, Santa Fe Institute, Santa Fe, NM. http://www.santafe.edu/projects/swarm/overview/overview.html.

    Google Scholar 

  • 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, 31 May 2007. http://www.math.uaa.alaska.edu/~orca/.

    Google Scholar 

  • Moffat J, Smith J and Witty S (2006). Emergent behaviour: theory and experimentation using the MANA model. J Appl Math Decis Sci 10: 1–13.

    Google Scholar 

  • NetLogo (2009). NetLogo home page. http://ccl.northwestern.edu/netlogo/.

    Google Scholar 

  • North M, Collier N and Vos J (2006). Experiences in creating three implementations of the repast agent modeling toolkit. ACM Trans Model Comp Simul 16(1): 1–25.

    Article  Google Scholar 

  • North M et al (2009). Multi-scale agent-based consumer market modeling. Complexity 15: 37–47.

    Google Scholar 

  • North MJ and Macal CM (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press: Oxford, UK.

    Book  Google Scholar 

  • North MJ, Tatara E, Collier NT and Ozik J (2007). Visual agent-based model development with repast simphony. In: Macal C, Sallach D and North M (eds). Proceedings of Agent 2007: Conference on Complex Interaction and Social Emergence. Chicago, IL, 7–9 October. http://www.agent2007.anl.gov/ pp 173–192.

    Google Scholar 

  • Olariu S and Zomaya AY (eds). (2006). Handbook of Bioinspired Algorithms. Chapman & Hall/CRC: Boca Raton, FL USA, p 679.

    Google Scholar 

  • Pan X, Han CS, Dauber K and Law KH (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc 22(2): 113–132.

    Article  Google Scholar 

  • Preziosi L (ed). (2003). Cancer Modelling and Simulation. Chapman and Hall/CRC: Boca Raton, FL.

    Google Scholar 

  • ROAD (Repast Organization for Architecture and Design) (2009). Repast home page. http://repast.sourceforge.net/.

  • Sallach D and Macal C (2001). The simulation of social agents: An introduction. Special Issue Soc Sci Comp Rev 19(3): 245–248.

    Article  Google Scholar 

  • Sakoda JM (1971). The checkerboard model of social interaction. J Math Sociol 1:119–132.

    Article  Google Scholar 

  • Sawyer RK (2005). Social Emergence: Societies ad Complex Systems. Cambridge University Press: Cambridge, UK.

    Book  Google Scholar 

  • Schelling TC (1978). Micromotives and Macrobehavior. Norton: New York.

    Google Scholar 

  • SDG (Swarm Development Group) (2009). Swarm development group home page. http://www.swarm.org/.

    Google Scholar 

  • Sun R. (ed). (2006). Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press: Cambridge.

    Google Scholar 

  • Tesfatsion L (2002). Agent-based computational economics: Growing economies from the bottom up. Artif Life 8(1): 55–82.

    Article  Google Scholar 

  • Tesfatsion L and Judd KL (eds). (2006). Handbook of Computational Economics, Volume II: Agent-Based Computational Economics. Elsevier/North-Holland: Amsterdam, p 904.

    Google Scholar 

  • Tonmukayakul A (2007). An agent-based model for secondary use of radio spectrum. PhD Thesis, School of Information Sciences, University of Pittsburgh.

    Google Scholar 

  • Troisi A, Wong V and Ratner M (2005). An agent-based approach for modeling molecular self-organization. Proc Natl Acad Sci 102(2): 255–260.

    Article  Google Scholar 

  • Weisbuch G (1991). Complex Systems Dynamics: An Introduction to Automata Networks (translated from French by S. Ryckebusch), Addison-Wesley: Redwood City, CA.

    Google Scholar 

  • Wilensky U (1999). Netlogo, Center for Connected Learning and Computer-Based Modeling. Northwestern University: Evanston, IL. http://ccl.northwestern.edu/netlogo/.

    Google Scholar 

  • Wilkinson TJ et al (2007). Modeling settlement systems in a dynamic environment. In: Kohler TA and Leeuw SEvd (eds). The Model-Based Archaeology of Socionatural Systems. School for Advanced Research Press: Santa Fe, NM, pp 175–208.

    Google Scholar 

  • Wooldridge M (2000). Reasoning About Rational Agents. MIT Press: Cambridge, MA.

    Google Scholar 

  • XJ Technologies (2009). AnyLogic home page. http://www.xjtek.com/.

    Google Scholar 

  • Yin L (2007). Assessing indirect spatial effects of mountain tourism development: An application of agent-based spatial modeling. J Region Anal Pol 37(3): 257–265. http://www.jrap-journal.org/pastvolumes/2000/v37/F37–3-8.pdf.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Argonne National Laboratory, Center for Complex Adaptive Agent Systems Simulation, Decision & Information Sciences Division, Argonne, Il, USA

    C. M. Macal & M. J. North

  2. Computation Institute, The University of Chicago, Chicago, Il, USA

    C. M. Macal & M. J. North

Authors
  1. C. M. Macal
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  2. M. J. North
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Editor information

Editors and Affiliations

  1. Brunel University, UK

    Simon J. E. Taylor

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© 2014 Operational Research Society

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Macal, C.M., North, M.J. (2014). Tutorial on agent-based modeling and simulation. In: Taylor, S.J.E. (eds) Agent-Based Modeling and Simulation. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137453648_2

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