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Assessing Multiagent Parcelization Performance in the MABEL Simulation Model Using Monte Carlo Replication Experiments

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  • Konstantinos Alexandridis
  • Bryan C Pijanowski

Abstract

In this paper we present and test the functionality of a parcelization algorithm, implemented in our spatially explicit, agent-based land-use-change model which we call the Multi Agent-based Behavioral Economic Landscape (MABEL) model. In order to test the best possible spatial configuration of the algorithm and its efficiency compared with historically observed land-use changes, we employed a Monte Carlo simulation approach with a series of replication experiments across time, and compared observed changes between 1970 and 1990, and across two different landscapes in Michigan, USA. We compare the simulated parcel shapes with historically observed land-use changes using the landscape-ecology metric program, FRAGSTATS.

Suggested Citation

  • Konstantinos Alexandridis & Bryan C Pijanowski, 2007. "Assessing Multiagent Parcelization Performance in the MABEL Simulation Model Using Monte Carlo Replication Experiments," Environment and Planning B, , vol. 34(2), pages 223-244, April.
    • Handle: RePEc:sae:envirb:v:34:y:2007:i:2:p:223-244
    DOI: 10.1068/b31181
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    References listed on IDEAS

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    1. Kleijnen, J.P.C., 2004. "Design and Analysis of Monte Carlo Experiments," Other publications TiSEM 7a12daae-c849-427e-8269-7, Tilburg University, School of Economics and Management.
    2. Elena G. Irwin, 2002. "Interacting agents, spatial externalities and the evolution of residential land use patterns," Journal of Economic Geography, Oxford University Press, vol. 2(1), pages 31-54, January.
    3. Olivier Barreteau & François Bousquet & Jean-Marie Attonaty, 2001. "Role-Playing Games for Opening the Black Box of Multi-Agent Systems: Method and Lessons of Its Application to Senegal River Valley Irrigated Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(2), pages 1-5.
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