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

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.

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File URL: http://jasss.soc.surrey.ac.uk/8/4/6/6.pdf
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Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 8 (2005)
Issue (Month): 4 ()
Pages: 6

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Handle: RePEc:jas:jasssj:2005-66-1
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  1. Romans Pancs & Nicolaas J. Vriend, 2003. "Schelling's Spatial Proximity Model of Segregation Revisited," Working Papers 487, Queen Mary University of London, School of Economics and Finance.
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  9. 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 2.
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  11. 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 11.
  12. 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 4.
  13. 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, March.
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