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Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

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Abstract

This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.

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  • J. Gareth Polhill & Lee-Ann Sutherland & Nicholas M. Gotts, 2010. "Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(2), pages 1-10.
    • Handle: RePEc:jas:jasssj:2009-9-3
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    2. Bert, Federico E. & Rovere, Santiago L. & Macal, Charles M. & North, Michael J. & Podestá, Guillermo P., 2014. "Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems," Ecological Modelling, Elsevier, vol. 273(C), pages 284-298.
    3. Malawska, Anna & Topping, Christopher John, 2016. "Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making," Agricultural Systems, Elsevier, vol. 143(C), pages 136-146.
    4. Sahat Hutajulu & Wawan Dhewanto & Eko Agus Prasetio, 2021. "An Agent-Based Model for 5G Technology Diffusion in Urban Societies: Simulating Two Development Scenarios," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    5. Kamila Štekerová & Josef Zelenka & Milan Kořínek, 2022. "Agent-Based Modelling in Visitor Management of Protected Areas," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    6. Sukaina Bharwani & Mònica Coll Besa & Richard Taylor & Michael Fischer & Tahia Devisscher & Chrislain Kenfack, 2015. "Identifying Salient Drivers of Livelihood Decision-Making in the Forest Communities of Cameroon: Adding Value to Social Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-3.
    7. Bert, Federico E. & Podestá, Guillermo P. & Rovere, Santiago L. & Menéndez, Ángel N. & North, Michael & Tatara, Eric & Laciana, Carlos E. & Weber, Elke & Toranzo, Fernando Ruiz, 2011. "An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas," Ecological Modelling, Elsevier, vol. 222(19), pages 3486-3499.
    8. Edmund Chattoe-Brown, 2013. "Why Sociology Should Use Agent Based Modelling," Sociological Research Online, , vol. 18(3), pages 31-41, August.

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