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

  • Borrill, Paul L.
  • Tesfatsion, Leigh

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. �

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File URL: http://www.econ.iastate.edu/sites/default/files/publications/papers/p11674-2010-07-06.pdf
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File URL: http://www.econ.iastate.edu/tesfatsi/ABMRightMath.PBLTWP.pdf
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 31674.

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Date of creation: 05 Jul 2010
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Handle: RePEc:isu:genres:31674
Contact details of provider: Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.eduEmail:


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  1. Paul L. Joskow, 2005. "Markets For Power In The United States - An Interim Assessment," Working Papers 0512, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
  2. 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.
  3. 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 8.
  4. Li, Hongyan & Tesfatsion, Leigh, 2009. "ISO Net Surplus Collection and Allocation in Wholesale Power Markets Under Locational Marginal Pricing," Staff General Research Papers 13092, Iowa State University, Department of Economics.
  5. LeBaron, Blake & Tesfatsion, Leigh S., 2008. "Modeling Macroeconomies As Open-Ended Dynamic Systems of Interacting Agents," Staff General Research Papers 12973, Iowa State University, Department of Economics.
  6. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
  7. Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh, 2010. "Testing Institutional Arrangements Via Agent-Based Modeling: A U.S. Electricity Market Example," Staff General Research Papers 13155, Iowa State University, Department of Economics.
  8. 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, vol. 34(4), pages 550-565.
  9. 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.
  10. Robert L. Axtell, 2000. "Effect of Interaction Topology and Activation Regime in Several Multi-Agent Systems," Working Papers 00-07-039, Santa Fe Institute.
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