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Agent based modeling for agricultural policy evaluation: A review


  • Dimitris Kremmydas

    () (Department of Agricultural Economics and Rural Development, Agricultural University of Athens, Iera Odos 75, Athens 11855, Greece)


In Agent-based computational economics economy is considered a complex system where the interactions between the economic agents are of ultimate importance. Simulating the economic system by modeling the behavior of the individual encompasses many advantages and certain epistemological issues are raised. In the analysis of Agricultural Policy, the agent based modeling (ABM) approach has been employed for studying Land Use Changes (LUCC), the dynamics of structural changes, the transmission of innovations, the simulation of water use management and for environmental modeling. This approach can help overcoming various simplifying assumptions of the traditional models (like the “homogenous agent” assumption) or the difficulty in modeling interactions. In this paper we initially do a short presentation of the principles of modeling economic systems with the ABM approach quoting its features, the advantages and disadvantages. Afterwards we make a discussion on the application of the ABM for modeling and evaluating agricultural policies and present four current application (Agripolis, Reg-MAS, MP-MAS, SWISSland). We finish this paper with some conclusions and suggestions.

Suggested Citation

  • Dimitris Kremmydas, 2012. "Agent based modeling for agricultural policy evaluation: A review," Working Papers 2012-3, Agricultural University of Athens, Department Of Agricultural Economics.
  • Handle: RePEc:aua:wpaper:2012-3

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    References listed on IDEAS

    1. Happe, Kathrin & Balmann, Alfons & Kellermann, Konrad & Sahrbacher, Christoph, 2008. "Does structure matter? The impact of switching the agricultural policy regime on farm structures," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 431-444, August.
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    4. Steven N. Durlauf, 1997. "What Should Policymakers Know About Economic Complexity?," Working Papers 97-10-080, Santa Fe Institute.
    5. Balmann, Alfons, 1997. "Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 24(1), pages 85-108.
    6. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 25(2-3), September.
    7. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    8. Kathrin Happe & Alfons Balmann & Konrad Kellermann, 2003. "Structural, efficiency and income effects of direct payments: an analysis of different payment schemes for the German region 'Hohenlohe'," Others 0308001, EconWPA.
    9. Stefania Bandini & Sara Manzoni & Giuseppe Vizzari, 2009. "Agent Based Modeling and Simulation: An Informatics Perspective," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-4.
    10. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    11. Appel, Franziska & Musshoff, Oliver, 2011. "How appropriate are myopic optimization models to predict decision behaviour: A comparison between agent-based models and business management games," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115994, European Association of Agricultural Economists.
    12. James Millington & Raúl Romero-Calcerrada & John Wainwright & George Perry, 2008. "An Agent-Based Model of Mediterranean Agricultural Land-Use/Cover Change for Examining Wildfire Risk," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(4), pages 1-4.
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    15. Kaye-Blake, William & Li, Frank Y. & Martin, A. McLeish & McDermott, Alan & Neil, Hayley & Rains, Scott, 2009. "A review of Multi-Agent Simulation Models in Agriculture," 2009 Conference, August 27-28, 2009, Nelson, New Zealand 97165, New Zealand Agricultural and Resource Economics Society.
    16. Tyler Freeman & James Nolan & Richard Schoney, 2009. "An Agent-Based Simulation Model of Structural Change in Canadian Prairie Agriculture, 1960-2000," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 537-554, December.
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    More about this item


    Agent based modeling; Agricultural policy evaluation; Agripolis; Reg-MAS; MP-MAS; SWISSland;

    JEL classification:

    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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