Agent-Based Model of Land-Use Decision Making∗
نویسندگان
چکیده
An agent-based model, incorporating a small set of primarily agent-based variables, was designed to explain land-use decision making. Agents are land-owners, who allocate their labour and land for different uses in regular time intervals. The goal is to understand what kind of spatial patterns emerge from different agent characteristics, and decision and learning mechanisms. Landscapes produced by the learning agent model are compared to actual land-cover data. By varying the parameter estimation schemes and the spatial metrics calculated from the simulated land-cover and the actual land-cover, the role of agent preferences for different land-uses is explored. The preliminary results suggest that the model captures relatively well the quantitative patterns of land-cover changes but it is poor in predicting the location of changes.
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