Keywords
human-environment interaction, agent-based modelling, land use change, adaptive decision-making, feedback loop learning
Start Date
1-7-2010 12:00 AM
Abstract
A key challenge of land-use modeling for supporting sustainable land management is to understand how environmental feedbacks emerged from land-use actions can reshape land-use decisions in the long-term. To investigate that issue, we use an agentbased land-use change model (LUDAS) developed by Le et al. [2008] based on a case study that was carried out in Hongha watershed (Vietnam). In LUDAS, goal-directed landuse decisions by household agents are explicitly modeled (i.e. agents calculate utilities for all land-use and location alternatives and likely select the alternative with highest utility). The model is run for two mechanisms of adaptation in land-use decisions to environmental changes that emerged from land-use actions. The first mechanism includes only a primary feedback loop learning, in which households adapt to the changing socio-ecological conditions by choosing the best land-use in the best location. The second mechanism builds on the first one but adds a secondary feedback loop learning, in which households can change their behavioural model in response to changing socio-ecological conditions. Patterns of land-use and interrelated community income changes driven from the two feedback mechanisms are compared to evaluate the added value of the inclusion of the secondary feedback loop learning. The results demonstrate that spatio-temporal signatures of the added feedback loops depend on domain type, time scale, and aggregation level of impact variables.
Modeling the adaptation of land-use decisions to landscape changes using an agent-based system: a case study in a mountainous catchment in central Vietnam
A key challenge of land-use modeling for supporting sustainable land management is to understand how environmental feedbacks emerged from land-use actions can reshape land-use decisions in the long-term. To investigate that issue, we use an agentbased land-use change model (LUDAS) developed by Le et al. [2008] based on a case study that was carried out in Hongha watershed (Vietnam). In LUDAS, goal-directed landuse decisions by household agents are explicitly modeled (i.e. agents calculate utilities for all land-use and location alternatives and likely select the alternative with highest utility). The model is run for two mechanisms of adaptation in land-use decisions to environmental changes that emerged from land-use actions. The first mechanism includes only a primary feedback loop learning, in which households adapt to the changing socio-ecological conditions by choosing the best land-use in the best location. The second mechanism builds on the first one but adds a secondary feedback loop learning, in which households can change their behavioural model in response to changing socio-ecological conditions. Patterns of land-use and interrelated community income changes driven from the two feedback mechanisms are compared to evaluate the added value of the inclusion of the secondary feedback loop learning. The results demonstrate that spatio-temporal signatures of the added feedback loops depend on domain type, time scale, and aggregation level of impact variables.
