Machine Learning - Special issue on learning with probabilistic representations
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Position Paper: Modelling with stakeholders
Environmental Modelling & Software
Land market mechanisms for preservation of space for coastal ecosystems: An agent-based analysis
Environmental Modelling & Software
Empirical characterisation of agent behaviours in socio-ecological systems
Environmental Modelling & Software
An agent-based simulation model of human-environment interactions in agricultural systems
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Spatial agent-based models for socio-ecological systems: Challenges and prospects
Environmental Modelling & Software
Hi-index | 0.00 |
We present an integrated modeling framework for simulating land-use decision making under the influence of payments for ecosystem services. The model combines agent-based modeling (ABM) with Bayesian belief networks (BBNs) and opinion dynamics models (ODM). The model endows agents with the ability to make land-use decisions at the household and plot levels. The decision-making process is captured with the BBNs that were constructed and calibrated with both qualitative and quantitative information, i.e., knowledge gained from group discussions with stakeholders and empirical survey data. To represent interpersonal interactions within social networks, the decision process is further modulated by the opinion dynamics model. The goals of the model are to improve the ability of ABM to emulate land-use decision making and thus provide a better understanding of the potential impacts of payments for ecosystem services on land use and household livelihoods. Our approach provides three important innovations. First, decision making is represented in a causal directed graph. Second, the model provides a natural framework for combining knowledge from experts and stakeholders with quantitative data. Third, the modular architecture and the software implementation can be customized with modest efforts. The model is therefore a flexible, general platform that can be tailored to other studies by mounting the appropriate case-specific ''brain'' into the agents. The model was calibrated for the Sloping Land Conversion Program (SLCP) in Yunnan, China using data from participatory mapping, focus group interviews, and a survey of 509 farm households in 17 villages.