Aggregating Value Functions: A Parameter-free, Uncertainty-aware Method to Elicit and Aggregate Value Functions from Multiple Experts in Multi Criteria Evaluation

[摘要] Even though Multi Criteria Evaluation (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 input criteria values. We propose a new method that allows to generate and aggregate 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 science, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a one-factor-at-a-time variation, secondly bootstrapping of the 13 inputs with subsequent analytical error propagation, and thirdly, a complete Monte Carlo simulation with the bootstrapped inputs. 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 relative uncertainty. In a validation workshop with 13 participants the results of the land use change case study were deemed plausible. To lessen the effects of the high spatial granularity inherent to MCEs, with abrupt changes of values from one pixel to the next, we applied Getis-Ord Gi* statistics. Hence, we isolated hotspots of high and low probability of continuing vineyard cultivation, respectively.

[学科分类] 计算机科学（综合）

[关键词] Multi Criteria Evaluation;Land Use Change;Value Functions;Sensitivity Analysis;Uncertainty;Monte Carlo Simulation