×J8. Modelling and decision making under uncertainty

Uncertainty is an intrinsic part of modelling and the legitimacy and utility of modelling for decision making is influenced by how different  sources of uncertainty are addressed and propagated through the model. Therefore, this session aims to share information on advances in  uncertainty and sensitivity analysis methods, approaches and case studies to promote explicit and reasoned consideration of uncertainty.  We welcome both quantitative and qualitative contributions, in both management and research settings.  Examples of quantitative techniques  include (but are not limited to) those associated with:

• deep uncertainty
• scenario analysis
• multi-criteria analysis
• exploratory modelling
• expert elicitation
• use of multiple working hypotheses
• multi-model ensembles
• sensitivity analysis

Examples of qualitative techniques include (but are not limited to):

• discussion and communication of limitations
• assessment of model pedigree
• assessment and quantification of information requirements
• identification of future research needs.

Key topics: Model assessment, Uncertainty, Sensitivity analysis, Robust decision making