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Influence Diagrams for Representing Uncertainty in Climate-Related Propositions

Abstract

In order to respond to policy questions about the potential impacts of climate change it is usually necessary to assemble large quantities of evidence from a variety of sources. Influence diagrams provide a formal mechanism for structuring this evidence and representing its relationship with the climate-related question of interest. When populated with probabilistic measures of belief an influence diagram provides a graphical representation of uncertainty, which can help to synthesize complex and contentious arguments into a relatively simple, yet evidence-based, graphical output.

Following unusually damaging floods in October–November 2000 the UK government commissioned research with a view to establishing the extent to which the floods were a manifestation of hydrological climate change. By way of example application, influence diagrams have been used to represent the evidential reasoning and uncertainties in responding to this question. Three alternative approaches to the mathematization of uncertainty in influence diagrams are demonstrated and compared. In situations of information scarcity and imprecise expert judgements, methods based on interval probabilities have proved to be attractive. Interval probabilities can, it is argued, represent ambiguity and ignorance in a more satisfactory manner than the conventional Bayesian alternative. The analysis provides a quantified commentary on the uncertainties in the conclusion that the events of October–November 2000 were extreme, but cannot in themselves be attributed to climate change.

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Correspondence to Jim Hall.

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Hall, J., Twyman, C. & Kay, A. Influence Diagrams for Representing Uncertainty in Climate-Related Propositions. Climatic Change 69, 343–365 (2005). https://doi.org/10.1007/s10584-005-2527-9

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  • DOI: https://doi.org/10.1007/s10584-005-2527-9

Keywords

  • Climate Change
  • Graphical Representation
  • Probabilistic Measure
  • Formal Mechanism
  • Expert Judgement