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Validation of an Agricultural MAS for Southland, New Zealand

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This paper describes the process and results of validating a simulation model of agriculture for a region in New Zealand. Validation is treated as a process, in which simulation models are made useful for specific purposes by making them conform to observed historical trends and relationships. In this case, the model was calibrated to reproduce the year-by-year conversion to dairying from 1993 to 2012 in Southland, New Zealand. This was achieved by holding constant some elements of the simulation model, based on economic theory or data, and by running simulations on a range of values for two key parameters. The paper describes the model and process, and demonstrates that empirical validation is possible if approached pragmatically with a view to the intended use of the model. Important elements are: using stylised facts to limit the parameter space ex ante, establishing the range of model outcomes and focusing on the most likely parameter space, focusing the search for parameter values where there is the greatest uncertainty, and using historical data to calibrate models.

Suggested Citation

  • Bill Kaye-Blake & Chris Schilling & Elizabeth Post, 2014. "Validation of an Agricultural MAS for Southland, New Zealand," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-5.
    • Handle: RePEc:jas:jasssj:2013-86-3
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    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. Suzi Kerr & Alex Olssen, 2012. "Gradual Land-use Change in New Zealand: Results from a Dynamic Econometric Model," Working Papers 12-06, Motu Economic and Public Policy Research.
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    2. Baillie, Sarah & Kaye-Blake, William & Smale, Paul & Dennis, Samuel, 2016. "Simulation modelling to investigate nutrient loss mitigation practices," Agricultural Water Management, Elsevier, vol. 177(C), pages 221-228.
    3. 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.
    4. 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).
    5. Fraser J Morgan & Adam J Daigneault, 2015. "Estimating Impacts of Climate Change Policy on Land Use: An Agent-Based Modelling Approach," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    6. Kaye-Blake, William & Schilling, Chris & Monaghan, Ross & Vibart, Ronaldo & Dennis, Samuel & Post, Elizabeth, 2019. "Quantification of environmental-economic trade-offs in nutrient management policies," Agricultural Systems, Elsevier, vol. 173(C), pages 458-468.
    7. Roberto Calisti & Primo Proietti & Andrea Marchini, 2019. "Promoting Sustainable Food Consumption: An Agent-Based Model About Outcomes of Small Shop Openings," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-2.

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