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Agent-Based Modeling: The Right Mathematics for the Social Sciences?

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  • Borrill, Paul L.
  • Tesfatsion, Leigh

Abstract

This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles.

Suggested Citation

  • Borrill, Paul L. & Tesfatsion, Leigh, 2010. "Agent-Based Modeling: The Right Mathematics for the Social Sciences?," Staff General Research Papers Archive 31674, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:31674
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    File URL: http://www2.econ.iastate.edu/papers/p11674-2010-07-06.pdf
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    File URL: http://www2.econ.iastate.edu/tesfatsi/ABMRightMath.PBLTWP.pdf
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    References listed on IDEAS

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    1. Paul L. Joskow, 2006. "Markets for Power in the United States: An Interim Assessment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-36.
    2. Robert L. Axtell, 2000. "Effect of Interaction Topology and Activation Regime in Several Multi-Agent Systems," Working Papers 00-07-039, Santa Fe Institute.
    3. Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337 Elsevier.
    4. Li, Hongyan & Tesfatsion, Leigh, 2009. "ISO Net Surplus Collection and Allocation in Wholesale Power Markets Under Locational Marginal Pricing," Staff General Research Papers Archive 13092, Iowa State University, Department of Economics.
    5. Nigel Gilbert & Andreas Pyka & Petra Ahrweiler, 2001. "Innovation Networks - a Simulation Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-8.
    6. Jason M Barr & Troy Tassier & Leanne J Ussher & Blake LeBaron & Shu-Heng Chen & Shyam Sunder, 2008. "The Future of Agent-Based Research in Economics: A Panel Discussion, Eastern Economic Association Annual Meetings, Boston, March 7, 20081," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 550-565.
    7. Dawid, Herbert, 2006. "Agent-based Models of Innovation and Technological Change," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 25, pages 1235-1272 Elsevier.
    8. Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh, 2010. "Testing Institutional Arrangements Via Agent-Based Modeling: A U.S. Electricity Market Example," Staff General Research Papers Archive 13155, Iowa State University, Department of Economics.
    9. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
    10. Blake LeBaron & Leigh Tesfatsion, 2008. "Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents," American Economic Review, American Economic Association, vol. 98(2), pages 246-250, May.
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    Citations

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    Cited by:

    1. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201702180800001022, Iowa State University, Department of Economics.
    2. Wozniak, Marcin, 2016. "Job placement agencies in an artificial labor market," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-54.
    3. Ali Naqvi & Miriam Rehm, 2014. "A multi-agent model of a low income economy: simulating the distributional effects of natural disasters," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 275-309, October.
    4. repec:spr:italej:v:3:y:2017:i:3:d:10.1007_s40797-017-0058-y is not listed on IDEAS
    5. Divine Odame APPIAH & Eric Kwabena FORKUO & John Tiah BUGRI, 2015. "Land Use Conversion Probabilities in a Peri-Urban District of Ghana," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-21, September.
    6. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201704300700001022, Iowa State University, Department of Economics.
    7. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201703280700001022, Iowa State University, Department of Economics.
    8. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201707110700001022, Iowa State University, Department of Economics.
    9. Фаттахов М.Р., 2013. "Агенто-Ориентированная Модель Социально-Экономического Развития Москвы," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 49(2), pages 30-43, апрель.
    10. repec:eee:wdevel:v:99:y:2017:i:c:p:395-418 is not listed on IDEAS

    More about this item

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D - Microeconomics
    • E - Macroeconomics and Monetary Economics

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