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Agent-based Modeling as a Bridge Between Disciplines

In: Handbook of Computational Economics

Author

Listed:
  • Axelrod, Robert

Abstract

Using the author's own experiences, this chapter shows how agent-based modeling (ABM) can address research questions common to many disciplines, facilitate interdisciplinary collaboration, provide a useful multidisciplinary tool when the math is intractable, and reveal unity across disciplines. While ABM can be a hard sell, convergence within the agent-based community can enhance the interdisciplinary value of the methodology.

Suggested Citation

  • Axelrod, Robert, 2006. "Agent-based Modeling as a Bridge Between Disciplines," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 33, pages 1565-1584, Elsevier.
    • Handle: RePEc:eee:hecchp:2-33
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    Citations

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    Cited by:

    1. Anand, Nilesh & van Duin, Ron & Tavasszy, Lorant, 2021. "Carbon credits and urban freight consolidation: An experiment using agent based simulation," Research in Transportation Economics, Elsevier, vol. 85(C).
    2. Richard Bookstaber, 2012. "Using Agent-Based Models for Analyzing Threats to Financial Stability," Working Papers 12-03, Office of Financial Research, US Department of the Treasury.
    3. Nilesh Anand & Ron van Duin & Lori Tavasszy, 2014. "Ontology-based multi-agent system for urban freight transportation," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(2), pages 133-153, July.
    4. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
    5. Eric Luis Uhlmann & Aleksey Korniychuk & Tomasz Obloj, 2018. "Initial prejudices create cross-generational intergroup mistrust," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-14, April.
    6. Asad Zaman, 2020. "New Directions in Macroeconomics," International Econometric Review (IER), Econometric Research Association, vol. 12(1), pages 1-23, April.
    7. Bichraoui-Draper, Najet & Xu, Ming & Miller, Shelie A. & Guillaume, Bertrand, 2015. "Agent-based life cycle assessment for switchgrass-based bioenergy systems," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 171-178.
    8. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling and Macroeconomics," ISU General Staff Papers 200601010800001585, Iowa State University, Department of Economics.
    9. Nan Lu, 2018. "La modélisation de l’indice CAC 40 avec un modèle basé agent," Erudite Ph.D Dissertations, Erudite, number ph18-02 edited by François Legendre, December.
    10. Filippo Neri, 2020. "How to Identify Investor's types in real financial markets by means of agent based simulation," Papers 2101.03127, arXiv.org.
    11. Michael J. Prietula & Daniel Conway, 2009. "The evolution of metanorms: quis custodiet ipsos custodes?," Computational and Mathematical Organization Theory, Springer, vol. 15(3), pages 147-168, September.
    12. Kaye-Blake, William & Schilling, Chris & Monaghan, Ross & Vibart, Ronaldo & Dennis, Samuel & Post, Elizabeth, 2019. "Quantification of environmental-economic trade-offs in nutrient management policies," Agricultural Systems, Elsevier, vol. 173(C), pages 458-468.
    13. Martin Klein & Ulrich J. Frey & Matthias Reeg, 2019. "Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-6.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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