Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

Abstract

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty.