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Getting Away From Numbers: Using Qualitative Observation For Agent-Based Modeling

Author

Listed:
  • LU YANG

    (Centre for Research in Social Simulation, Department of Sociology, University of Surrey, Guildford, Surrey, GU2 7XH, UK)

  • NIGEL GILBERT

    (Centre for Research in Social Simulation, Department of Sociology, University of Surrey, Guildford, Surrey, GU2 7XH, UK)

Abstract

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

Suggested Citation

  • Lu Yang & Nigel Gilbert, 2008. "Getting Away From Numbers: Using Qualitative Observation For Agent-Based Modeling," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 175-185.
    • Handle: RePEc:wsi:acsxxx:v:11:y:2008:i:02:n:s0219525908001556
    DOI: 10.1142/S0219525908001556
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    Cited by:

    1. Elsenbroich, Corinna & Payette, Nicolas, 2020. "Choosing to cooperate: Modelling public goods games with team reasoning," Journal of choice modelling, Elsevier, vol. 34(C).
    2. Ahmadreza Asgharpourmasouleh & Atiye Sadeghi & Ali Yousofi, 2017. "A Grounded Agent-Based Model of Common Good Production in a Residential Complex: Applying Artificial Experiments," SAGE Open, , vol. 7(4), pages 21582440177, October.
    3. Martin Neumann, 2015. "Grounded Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-9.
    4. Amineh Ghorbani & Gerard Dijkema & Noortje Schrauwen, 2015. "Structuring Qualitative Data for Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-2.
    5. Elizabeth Bruch & Jon Atwell, 2015. "Agent-Based Models in Empirical Social Research," Sociological Methods & Research, , vol. 44(2), pages 186-221, May.
    6. 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.
    7. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).
    8. Javier Arroyo & Samer Hassan & Celia Gutiérrez & Juan Pavón, 2010. "Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 16(4), pages 416-435, December.
    9. Shah Jamal Alam & Armando Geller & Ruth Meyer & Bogdan Werth, 2010. "Modelling Contextualized Reasoning in Complex Societies with "Endorsements"," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-6.

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