Keywords
uncertainty elicitation, logic-based ecological modelling, metadata
Start Date
1-7-2004 12:00 AM
Abstract
Uncertainty is an intrinsic feature of complex ecological models. Given that it is not possible to rid the modelsfrom uncertainty, we are left with taking notice of it for consideration in model-based decision making. Traditionalecological modelling methods and tools do not support explicit accounts of model uncertainty. Thiswork gives a contribution towards making known, or bringing to the surface, sources of uncertainty that areembedded in ecological models. The sources of uncertainty are related to the models’ supporting data andequations. A metadata standard is used to specify data-related sources of uncertainty, such as creator and coverage.In the technique developed, models are described and simulated using logic, which allows the sourcesof uncertainty to be easily represented, and later propagated and combined during simulation. The combinedsources of uncertainty can then be presented to the user who can assess their impact on model outputs and tuneup his confidence in the model for decision making.
Eliciting Sources of Uncertainty in Ecological Simulation Models
Uncertainty is an intrinsic feature of complex ecological models. Given that it is not possible to rid the modelsfrom uncertainty, we are left with taking notice of it for consideration in model-based decision making. Traditionalecological modelling methods and tools do not support explicit accounts of model uncertainty. Thiswork gives a contribution towards making known, or bringing to the surface, sources of uncertainty that areembedded in ecological models. The sources of uncertainty are related to the models’ supporting data andequations. A metadata standard is used to specify data-related sources of uncertainty, such as creator and coverage.In the technique developed, models are described and simulated using logic, which allows the sourcesof uncertainty to be easily represented, and later propagated and combined during simulation. The combinedsources of uncertainty can then be presented to the user who can assess their impact on model outputs and tuneup his confidence in the model for decision making.
