  The aim of this paper is to present and describe SimLab 1.1 (Simulation
Laboratory for Uncertainty and Sensitivity Analysis) software designed for
Monte Carlo analysis that is based on performing multiple model evaluations
with probabilistically selected model input. The results of these evaluations
are used to determine both the uncertainty in model predictions and the input
variables that drive this uncertainty. This methodology is essential in
situations where a decision has to be taken based on the model results; typical
examples include risk and emergency management systems, financial analysis and
many others. It is also highly recommended as part of model validation, even
where the models are used for diagnostic purposes, as an element of sound model
building. SimLab allows an exploration of the space of possible alternative
model assumptions and structure on the prediction of the model, thereby testing
both the quality of the model and the robustness of the model based inference.
