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Simulation vs. Definition: Differing Approaches to Setting Probabilities for Agent Behaviour

Author

Listed:
  • Fraser J. Morgan

    (Landcare Research New Zealand, Private Bag 92170, Auckland Mail Centre, Auckland 1142, New Zealand)

  • Philip Brown

    (Landcare Research New Zealand, P.O. Box 69040, Lincoln 7640, New Zealand)

  • Adam J. Daigneault

    (Landcare Research New Zealand, Private Bag 92170, Auckland Mail Centre, Auckland 1142, New Zealand)

Abstract

While geographers and economists regularly work together on the development of land-use and land-cover change models, research on how differences in their modelling approaches affects the results is rare. Answering calls for more coordination between the two disciplines in order to build models that better represent the real world, we (two economists and a geographer) developed an economically grounded, spatially explicit, agent-based model to explore the effects of environmental policy on rural land use in New Zealand. This inter-disciplinary collaboration raised a number of differences in modelling approach. One key difference, and the focus of this paper, is the way in which processes that shape the behaviour of agents are integrated within the model. Using the model and a nationally representative survey, we compare the land-use effects of two disciplinary-aligned approaches to setting a farmer agent’s likelihood of land-use conversion. While we anticipated that the approaches would significantly affect model outcomes, at a catchment scale they produced similar trends and results. However, further analysis at a sub-catchment scale suggests the approach to setting the likelihood of land-use conversion does matter. While the results outlined here will not fully resolve the disciplinary differences, they do outline the need to account for heterogeneity in the predicted agent behaviours for both disciplines.

Suggested Citation

  • Fraser J. Morgan & Philip Brown & Adam J. Daigneault, 2015. "Simulation vs. Definition: Differing Approaches to Setting Probabilities for Agent Behaviour," Land, MDPI, vol. 4(4), pages 1-24, September.
  • Handle: RePEc:gam:jlands:v:4:y:2015:i:4:p:914-937:d:56465
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    References listed on IDEAS

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    Cited by:

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    3. James D. A. Millington & Hang Xiong & Steve Peterson & Jeremy Woods, 2017. "Integrating Modelling Approaches for Understanding Telecoupling: Global Food Trade and Local Land Use," Land, MDPI, vol. 6(3), pages 1-18, August.
    4. Philip Brown, 2019. "Gender, Educational Attainment, and Farm Outcomes in New Zealand," Land, MDPI, vol. 8(1), pages 1-16, January.
    5. Tesfatsion, Leigh & Jie, Yu & Rehmann, Chris R. & Gutowski, William J., 2015. "WACCShed: A Platform for the Study of Watersheds as Dynamic Coupled Natural and Human Systems," ISU General Staff Papers 201512160800001226, Iowa State University, Department of Economics.
    6. Gawith, David & Hodge, Ian & Morgan, Fraser & Daigneault, Adam, 2020. "Climate change costs more than we think because people adapt less than we assume," Ecological Economics, Elsevier, vol. 173(C).
    7. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    8. Ainsworth, Penelope & Bell, Kendon & Barker, Adam, 2024. "An agent-based approach to QUICKly valuing the benefits of agricultural research and extension," Agricultural Systems, Elsevier, vol. 216(C).

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