Characterising Uncertainty in Expert Assessments: Encoding Heavily Skewed Judgements.

Abstract

When limited or no observed data are available, it is often useful to obtain expert knowledge about parameters of interest, including point estimates and the uncertainty around these values. However, it is vital to elicit this information appropriately in order to obtain valid estimates. This is particularly important when the experts' uncertainty about these estimates is strongly skewed, for instance when their best estimate is the same as the lowest value they consider possible. Also this is important when interest is in the aggregation of elicited values. In this paper, we compare alternative distributions for describing such estimates. The distributions considered include the lognormal, mirror lognormal, Normal and scaled Beta. The case study presented here involves estimation of the number of species in coral reefs, which requires eliciting counts within broader taxonomic groups, with highly skewed uncertainty estimates. This paper shows substantial gain in using the scaled Beta distribution, compared with Normal or lognormal distributions. We demonstrate that, for this case study on counting species, applying the novel encoding methodology developed in this paper can facilitate the acquisition of more rigorous estimates of (hierarchical) count data and credible bounds. The approach can also be applied to the more general case of enumerating a sampling frame via elicitation.