Article

Tutorial on agent-based modeling and simulation part 2: how to model with agents

Authors:

Charles M. Macal,

Michael J. NorthAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 73 - 83

Published: 03 December 2006 Publication History

Abstract

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of interacting autonomous agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to do research. Some have gone so far as to contend that ABMS is a new way of doing science. Computational advances make possible a growing number of agent-based applications across many fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling the growth and decline of ancient civilizations to modeling the complexities of the human immune system, and many more. This tutorial describes the foundations of ABMS, identifies ABMS toolkits and development methods illustrated through a supply chain example, and provides thoughts on the appropriate contexts for ABMS versus conventional modeling techniques.

References

[1]

AnyLogic. 2006. <http://www.xjtek.com/>.

[2]

Arthur, W. B., S. N. Durlauf, and D. A. Lane (eds.) 1997. The economy as an evolving complex system II, SFI Studies in the Sciences of Complexity, Addison Wesley: Reading, MA.

[3]

Axelrod, R. 1997. The complexity of cooperation: agentbased models of competition and collaboration, Princeton, NJ: Princeton University Press.

[4]

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, D.C.

[5]

Barabási, A.-L. 2002. Linked: the new science of networks, Cambridge, MA: Perseus Pub.

[6]

Bonabeau, E., M. Dorigo and G. Theraulaz. 1999. Swarm intelligence: from natural to artificial systems, Oxford: Oxford University Press.

Digital Library

[7]

Bonabeau, E. 2001. Agent-based modeling: methods and techniques for simulating human systems. In Proc. National Academy of Sciences 99(3): 7280--7287.

[8]

Booch, G., J. Rumbaugh and I. Jacobson. 1998. The Unified Modeling Language User Guide, Addison-Wesley:New York.

Digital Library

[9]

Carley, K. 2006. BioDefense through City Level Multi-Agent Modeling of Bio and Chemical Threats. Arizona Spring Biosurveillance Workshop, Tucson, Arizona.

[10]

Casti, J. 1997. Would-be worlds: how simulation is changing the world of science, New York: Wiley.

Digital Library

[11]

Cirillo, R., P. Thimmapuram, T. Veselka, V. Koritarov, G. Conzelmann, C. Macal, G. Boyd, M. North, T. Overbye and X. Cheng. 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.

[12]

Epstein, J. M. and R. Axtell. 1996. Growing artificial societies: social science from the bottom up. Cambridge, MA: MIT Press.

Digital Library

[13]

Fang, C., S. O. Kimbrough, A. Valluri, and Z. Zheng. 2002. On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt. Group Decision and Negotiation, 11(6): 449--467.

[14]

FIPA (Foundation for Intelligent Physical Agents). 2005. FIPA Home Page, <http://www.fipa.org/>.

[15]

Folcik, V. and C. G. Orosz. 2006. An Agent-based Model Demonstrates That the Immune System Behaves Like a Complex System and a Scale-Free Network. SwarmFest 2006, University of Notre Dame, South Bend, IN, June.

[16]

Gilbert, N. and K. G. Troitzsch. 1999. Simulation for the Social Scientist, Buckingham UK: Open University Press.

Digital Library

[17]

GMU (George Mason University). 2006. <http://cs.gmu.edu/~eclab/projects/mason/>.

[18]

Grimm, V., U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, T. Grand, S. K. Heinz and G. Huse. 2006. A Standard Protocol for Describing Individual-Based and Agent-Based Models. Ecological Modelling. {In Press.}

[19]

Holland, J. H., 1995, Hidden order: how adaptation builds complexity. Reading, MA: Addison-Wesley.

Digital Library

[20]

Huang, C.-Y., C.-T. Sun, J.-L. Hsieh, and H. Lin. 2004. Simulating SARS: Small-world epidemiological modeling and public health policy assessments. Journal of Artificial Societies and Social Simulation 7(4): 100--131.

[21]

Jennings, N. R. 2000. On agent-based software engineering. Artificial Intelligence, 117:277--296.

Digital Library

[22]

Koritorov, V., 2004, Real-World Market Representation with Agents, IEEE Power and Energy Magazine:39--46.

[23]

Kohler, T. A., G. J. Gumerman and R. G. Reynolds. 2005. Simulating ancient societies. Scientific American. July.

[24]

Law, A. M. and D. W. Kelton. 2000. Simulation modeling and analysis. 3rd ed. New York: McGraw-Hill.

Digital Library

[25]

LeBaron, B. (2002). Short-memory traders and their impact on group learning in financial markets. Proc. National Academy of Sciences 99(90003): 7201--7206.

[26]

Minar, N., R. Burkhart, C. Langton, and M. Askenazi. 1996. The Swarm simulation system, a toolkit for building multi-agent simulations, <http://www.santafe.edu/projects/swarm/overview/overview.html>.

[27]

Macal, C., and M. North. 2003. Effects of global information availability in networks of supply chain agents. Proc. Agent 2003: Conf. on Challenges in Social Simulation, Eds., C. Macal, D. Sallach and M. North, Chicago, IL, Oct. 2--4, 235--252, Argonne National Laboratory.

[28]

Macal, C. 2004. Emergent structures from trust relationships in supply chains. Proc. Agent 2004: Conf. on Social Dynamics, Eds., C. Macal, D. Sallach and M. North, Chicago, IL, Oct. 7--9, 743--760, Argonne National Laboratory.

[29]

NetLogo. 2006. NetLogo home page. <http://ccl.northwestern.edu/netlogo>.

[30]

North, M. J., and C. M. Macal. {In press.} Managing business complexity: discovering strategic solutions with agent-based modeling and simulation, Oxford: Oxford University Press.

Digital Library

[31]

North, M. J., N. T. Collier, and J. R. Vos. 2006. Experiences in Creating Three Implementations of the Repast Agent Modeling Toolkit, ACM Transactions on Modeling and Computer Simulation, 16(1):1--25, January.

Digital Library

[32]

North, M. J. and C. M. Macal. 2005. Escaping the accidents of history: an overview of artificial life modeling with Repast, in Artificial Life Models in Software, Eds., A. Adamatzky and M. Komosinski, Springer-Verlag: Dordrecht, Netherlands.

[33]

NRC (National Research Council). 2003. Dynamic social network modeling and analysis: workshop summary and papers, R. Brieger, K. Carley, and P. Pattison, Committee on Human Factors, Washington, DC: National Academies Press.

[34]

Repast. 2006. Repast home page. <http://repast.sourceforge.net/>.

[35]

Reynolds, Craig. 2006. Boids. <http://www.red3d.com/cwr/boidss/>.

[36]

Sallach, D. and C. Macal. 2001. The simulation of social agents: an introduction. Social Science Computer Review 19(3):245--248.

[37]

Schelling, T. C. 1978. Micromotives and macrobehavior. New York: Norton.

[38]

SDG (Swarm Development Group). 2006. Swarm Development Group home page. <http://www.swarm.org>.

[39]

Simon, H. 2001. The sciences of the artificial, Cambridge, MA: MIT Press.

Digital Library

[40]

Tesfatsion, L. 2005. Agent-based Computational Economics (ACE) home page. <http://www.econ.iastate.edu/tesfatsi/ace.htm>.

[41]

Tobias, R. and C. Hofmann. 2004. Evaluation of free Javalibraries for social-scientific agent based simulation. Journal of Artificial Societies and Social Simulation. 7(1), Jan. 31.

Cited By

Laite RPortman NSankaranarayanan KHuschka TChick S(2016)Behavioral analysis of agent based service channel design using neural networksProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042557(3694-3695)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042557
Nwokoye CEjiofor VOrji RMbeledeogu N(2016)The Topicality of Agent-Based Modeling/Multi Agent Systems in Human Computer Interaction ResearchProceedings of the First African Conference on Human Computer Interaction10.1145/2998581.2998593(80-91)Online publication date: 21-Nov-2016
https://dl.acm.org/doi/10.1145/2998581.2998593
Zhu TMera JSuarez BMaroto J(2016)An agent-based support system for railway station dispatchingExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.05.01161:C(39-52)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.05.011
Show More Cited By

Tutorial on agent-based modeling and simulation part 2: how to model with agents
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
  2. Modeling and simulation
    1. Model development and analysis

Recommendations

Tutorial on agent-based modeling and simulation
WSC '05: Proceedings of the 37th conference on Winter simulation

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers ...
Tutorial on agent-based modeling and simulation: ABM design for the zombie apocalypse
WSC '18: Proceedings of the 2018 Winter Simulation Conference

Agent-based modeling (ABM) and simulation is an approach to modeling systems comprised of autonomous, interacting agents. Computational advances are making it possible to develop agent-based models in a variety of application areas, including areas ...
Agent-based modeling and simulation: ABMS examples
WSC '08: Proceedings of the 40th Conference on Winter Simulation

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

Sponsors

IIE: Institute of Industrial Engineers
ASA: American Statistical Association
IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE-CS\DATC: The IEEE Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International
INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

Publisher

Winter Simulation Conference

Publication History

Published: 03 December 2006

Check for updates

Qualifiers

Article

Conference

WSC06

Sponsor:

IIE
ASA
IEICE ESS
IEEE-CS\DATC
SIGSIM
NIST
(SCS)
INFORMS-CS

WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

Acceptance Rates

WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
2,444
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Laite RPortman NSankaranarayanan KHuschka TChick S(2016)Behavioral analysis of agent based service channel design using neural networksProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042557(3694-3695)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042557
Nwokoye CEjiofor VOrji RMbeledeogu N(2016)The Topicality of Agent-Based Modeling/Multi Agent Systems in Human Computer Interaction ResearchProceedings of the First African Conference on Human Computer Interaction10.1145/2998581.2998593(80-91)Online publication date: 21-Nov-2016
https://dl.acm.org/doi/10.1145/2998581.2998593
Zhu TMera JSuarez BMaroto J(2016)An agent-based support system for railway station dispatchingExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.05.01161:C(39-52)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.05.011
Kang AKim DLee JBae JMoon I(2015)Comparative study of command and control structure between rok and us field artillery battalionProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888894(2400-2411)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888894
Husselmann A(2015)Data-parallel structural optimisation in agent-based modelsACM SIGEVOlution10.1145/2815474.28154817:2-3(33-36)Online publication date: 17-Aug-2015
https://dl.acm.org/doi/10.1145/2815474.2815481
Cea-Olvera FRamirez-Nafarrate AAlbizuri-Romero BTaylor SMustafee NSon Y(2015)An Agent-Based Simulation Model to Evaluate the Efficiency of a Synthetic Biology SystemProceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/2769458.2769466(127-132)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1145/2769458.2769466
Taylor SAnagnostou AKiss TTerstyanszky GKacsuk PFantini NBuckley SMiller J(2014)A tutorial on cloud computing for agent-based modeling & simulation with repastProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693884(192-206)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693884
Bajracharya KDuboz RWainer GMosterman PBarros FZacharewicz G(2013)Comparison of three agent-based platforms on the basis of a simple epidemiological model (WIP)Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium10.5555/2499634.2499641(1-6)Online publication date: 7-Apr-2013
https://dl.acm.org/doi/10.5555/2499634.2499641
Shukla NMunoz AMa JHuynh NWainer GMosterman PBarros FZacharewicz G(2013)Hybrid agent based simulation with adaptive learning of travel mode choices for university commuters (WIP)Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium10.5555/2499634.2499636(1-6)Online publication date: 7-Apr-2013
https://dl.acm.org/doi/10.5555/2499634.2499636
Husselmann AHawick K(2013)Simulating growth kinetics in a data-parallel 3d lattice photobioreactorModelling and Simulation in Engineering10.1155/2013/1532412013(20-20)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1155/2013/153241
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten