IDEAS home Printed from https://ideas.repec.org/a/sae/simgam/v42y2011i2p243-264.html

A Plea for (Good) Simulations: Nudging Economics Toward an Experimental Science

Author

Listed:
  • Julian Reiss

    (Erasmus University, Rotterdam, Netherlands, reiss@fwb.eur.nl)

Abstract

In this article, the author argues that simulation is an undervalued technique to draw conclusions about empirical phenomena in economics. If the aim is to learn about the behavior of socioeconomic systems of interest, simulations have a variety of advantages relative to alternatives such as mathematical (pen and paper) modeling and laboratory experimentation. Therefore, the author has a good prima facie reason to exploit this method more fully. The author proceed by demonstrating that frequently heard arguments against simulations are wrong, and finally the author discusses a number of more specific empirical phenomena, criticisms of one type of simulation methodology used in economics.

Suggested Citation

  • Julian Reiss, 2011. "A Plea for (Good) Simulations: Nudging Economics Toward an Experimental Science," Simulation & Gaming, , vol. 42(2), pages 243-264, April.
    • Handle: RePEc:sae:simgam:v:42:y:2011:i:2:p:243-264
    DOI: 10.1177/1046878110393941
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1046878110393941
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1046878110393941?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cartwright,Nancy, 2007. "Hunting Causes and Using Them," Cambridge Books, Cambridge University Press, number 9780521860819, Enero-Abr.
    2. Hoover, Kevin D, 1995. "Facts and Artifacts: Calibration and the Empirical Assessment of Real-Business-Cycle Models," Oxford Economic Papers, Oxford University Press, vol. 47(1), pages 24-44, January.
    3. Lars Peter Hansen & James J. Heckman, 1996. "The Empirical Foundations of Calibration," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 87-104, Winter.
    4. Cartwright,Nancy, 2007. "Hunting Causes and Using Them," Cambridge Books, Cambridge University Press, number 9780521677981, Enero-Abr.
    5. Magda Fontana, 2006. "Simulation in Economics: Evidence on Diffusion and Communication," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(2), pages 1-8.
    6. Ray C. Fair & Arnold Zellner (ary), 1992. "The Cowles Commission approach, real business cycles theories, and New- Keynesian economics," Proceedings, Federal Reserve Bank of St. Louis, pages 133-157.
    7. Guala,Francesco, 2005. "The Methodology of Experimental Economics," Cambridge Books, Cambridge University Press, number 9780521618618, Enero-Abr.
    8. Gabriel Wainer & Qi Liu & Olivier Dalle & Bernard P. Zeigler, 2010. "Applying Cellular Automata and DEVS Methodologies to Digital Games: A Survey," Simulation & Gaming, , vol. 41(6), pages 796-823, December.
    9. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Evgeniya Duzhak & Jody Hoff & Jane S. Lopus, 2021. "The Effects of the Chair the Fed Simulation on High School Students’ Knowledge," The American Economist, Sage Publications, vol. 66(1), pages 74-89, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Galbács Peter, 2021. "What did it take for Lucas to set up ‘useful’ analogue systems in monetary business cycle theory?," Economics and Business Review, Sciendo, vol. 7(3), pages 61-82, September.
    2. Ricardo F. Crespo, 2012. "Models as signs" as "good economic models," Estudios Economicos, Universidad Nacional del Sur, Departamento de Economia, vol. 29(58), pages 1-12, january-j.
    3. Andrew Gelman & Guido Imbens, 2013. "Why ask Why? Forward Causal Inference and Reverse Causal Questions," NBER Working Papers 19614, National Bureau of Economic Research, Inc.
    4. Patrick Fève, 1997. "Les méthodes d'étalonnage au regard de l'économétrie," Revue Économique, Programme National Persée, vol. 48(3), pages 629-638.
    5. Courgeau, Daniel, 2012. "Probability and social science : methodologial relationships between the two approaches ?," MPRA Paper 43102, University Library of Munich, Germany.
    6. Jovanovic, Franck & Schinckus, Christophe, 2016. "Breaking down the barriers between econophysics and financial economics," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 256-266.
    7. Adrian Pabst & Roberto Scazzieri, 2012. "The political economy of civil society," Constitutional Political Economy, Springer, vol. 23(4), pages 337-356, December.
    8. Michael Margolis, 2017. "Graphs as a Tool for the Close Reading of Econometrics (Settler Mortality is not a Valid Instrument for Institutions)," Economic Thought, World Economics Association, vol. 6(1), pages 56-82, March.
    9. Mouchart, Michel & Russo, Federica & Wunsch, Guillaume, 2011. "Inferring causal relations by modelling structures : Article de recherche," LIDAM Discussion Papers ISBA 2011007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Emily Chamlee-Wright, 2010. "Qualitative methods and the pursuit of economic understanding," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 23(4), pages 321-331, December.
    11. Pearl Judea, 2018. "Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes," Journal of Causal Inference, De Gruyter, vol. 6(2), pages 1-7, September.
    12. Senbeta, Sisay, 2011. "How applicable are the new keynesian DSGE models to a typical low-income economy?," MPRA Paper 30931, University Library of Munich, Germany.
    13. Padró, R. & Marco, I. & Font, C. & Tello, E., 2019. "Beyond Chayanov: A sustainable agroecological farm reproductive analysis of peasant domestic units and rural communities (Sentmenat; Catalonia, 1860)," Ecological Economics, Elsevier, vol. 160(C), pages 227-239.
    14. Priscilla Álamos-Concha & Valérie Pattyn & Benoît Rihoux & Benjamin Schalembier & Derek Beach & Bart Cambré, 2022. "Conservative solutions for progress: on solution types when combining QCA with in-depth Process-Tracing," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 1965-1997, August.
    15. Cozzi, Marco, 2014. "Equilibrium Heterogeneous-Agent models as measurement tools: Some Monte Carlo evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 208-226.
    16. Paul Shaffer, 2018. "Causal pluralism and mixed methods in the analysis of poverty dynamics," WIDER Working Paper Series 115, World Institute for Development Economic Research (UNU-WIDER).
    17. LeRoy, Stephen F, 2024. "Reconsidering Causation," University of California at Santa Barbara, Economics Working Paper Series qt12q3t2vd, Department of Economics, UC Santa Barbara.
    18. Deirdre Nansen McCloskey, 2013. "Why Economics cannot Explain the Modern World," The Economic Record, The Economic Society of Australia, vol. 89, pages 8-22, June.
    19. Hünermund Paul & Louw Beyers & Caspi Itamar, 2023. "Double machine learning and automated confounder selection: A cautionary tale," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-12, January.
    20. Liu, Jing & Arndt, Channing, 2004. "Parameter Estimation and Measures of Fit in A Global, General Equilibrium Model," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 19, pages 626-649.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:simgam:v:42:y:2011:i:2:p:243-264. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.