IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2005-66-1.html
   My bibliography  Save this article

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

Author

Abstract

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.

Suggested Citation

  • Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
  • Handle: RePEc:jas:jasssj:2005-66-1
    as

    Download full text from publisher

    File URL: http://jasss.soc.surrey.ac.uk/8/4/6/6.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Thomas Brenner & Claudia Werker, 2004. "Empirical Calibration of Simulation Models," Computing in Economics and Finance 2004 89, Society for Computational Economics.
    2. Vito Albino & Nunzia Carbonara & Ilaria Giannoccaro, 2003. "Coordination Mechanisms Based on Cooperation and Competition Within Industrial Districts: an Agent-Based Computational Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(4), pages 1-3.
    3. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
    4. Pancs, Romans & Vriend, Nicolaas J., 2007. "Schelling's spatial proximity model of segregation revisited," Journal of Public Economics, Elsevier, vol. 91(1-2), pages 1-24, February.
    5. Bruce Edmonds & David Hales, 2003. "Replication, Replication and Replication: Some Hard Lessons from Model Alignmen," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(4), pages 1-11.
    6. J. Gary Polhill & Luis R. Izquierdo & Nicholas M. Gotts, 2004. "The Ghost in the Model (and Other Effects of Floating Point Arithmetic)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(1), pages 1-5.
    7. Thomas Brenner, 2001. "Simulating the Evolution of Localised Industrial Clusters - an Identification of the Basic Mechanisms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-4.
    8. Arianna Dal Forno & Ugo Merlone, 2004. "From Classroom Experiments to Computer Code," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(3), pages 1-2.
    9. Junfu Zhang, 2004. "Growing Silicon Valley On A Landscape: An Agent-Based Approach To High-Tech Industrial Clusters," World Scientific Book Chapters,in: Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 13, pages 259-283 World Scientific Publishing Co. Pte. Ltd..
    10. Andreas Pyka & Petra Ahrweiler, 2004. "Applied Evolutionary Economics and Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(2), pages 1-6.
    11. Michael Prietula & Kathleen Carley & Les Gasser (ed.), 1998. "Simulating Organizations: Computational Models of Institutions and Groups," MIT Press Books, The MIT Press, edition 1, volume 1, number 026266108x, January.
    12. Scott Moss, 1998. "Critical Incident Management: an Empirically Derived Computational Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(4), pages 1-1.
    13. T. Brenner & P. Murmann, 2003. "The Use of Simulations in Developing," Papers on Economics and Evolution 2003-03, Philipps University Marburg, Department of Geography.
    14. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, January.
    15. Guido Fioretti, 2002. "Information Structure and Behaviour of a Textile Industrial District," Urban/Regional 0207003, EconWPA, revised 13 Aug 2002.
    16. Francesca Borrelli & Cristina Ponsiglione & Luca Iandoli & Giuseppe Zollo, 2005. "Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(3), pages 1-4.
    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. Vermeulen, Ben & Pyka, Andreas, 2016. "Agent-based modeling for decision making in economics under uncertainty," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-33.
    2. Jasper Muis, 2010. "Simulating Political Stability and Change in the Netherlands (1998-2002): an Agent-Based Model of Party Competition with Media Effects Empirically Tested," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(2), pages 1-4.
    3. Li, Jinjing & O'Donoghue, Cathal, 2012. "A methodological survey of dynamic microsimulation models," MERIT Working Papers 002, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    4. Ozge Dilaver, 2015. "From Participants to Agents: Grounded Simulation as a Mixed-Method Research Design," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-15.
    5. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    6. J. Gary Polhill & Dawn C. Parker & Daniel Brown & Volker Grimm, 2008. "Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-3.
    7. Dan Farhat, 2011. "Bookworms versus Party Animals: An Artificial Labor Market with Human and Social Capital Accumulation," Working Papers 1103, University of Otago, Department of Economics, revised May 2011.
    8. Gönenç Yücel & Els van Daalen, 2009. "An Objective-Based Perspective on Assessment of Model-Supported Policy Processes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-3.
    9. Anna Klabunde & Frans Willekens, 2016. "Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges," European Journal of Population, Springer;European Association for Population Studies, vol. 32(1), pages 73-97, February.
    10. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    11. repec:pal:jorsoc:v:68:y:2017:i:5:d:10.1057_s41274-016-0022-5 is not listed on IDEAS
    12. Kostadinov, Fabian & Holm, Stefan & Steubing, Bernhard & Thees, Oliver & Lemm, Renato, 2014. "Simulation of a Swiss wood fuel and roundwood market: An explorative study in agent-based modeling," Forest Policy and Economics, Elsevier, vol. 38(C), pages 105-118.
    13. Squazzoni, Flaminio & Gandelli, Claudio, 2012. "Saint Matthew strikes again: An agent-based model of peer review and the scientific community structure," Journal of Informetrics, Elsevier, vol. 6(2), pages 265-275.

    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:jas:jasssj:2005-66-1. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Flaminio Squazzoni). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.