Analyzing Uncertainties in Experts' Opinions of Forest Plan Performance

Abstract

Abstract Multi-objective forestry requires new decision support systems to aid the forest owner and foresters in the planning of future treatment schedules. The analytic hierarchy process (AHP), based on pairwise comparison data and Saaty's eigenvector method, is one technique that has been proposed to make such qualitatively different objectives as income from timber sales and scenic beauty of forest landscape commensurable. A weak point of the methodology has been the lack of a statistical theory behind it. We have earlier shown how classical regression techniques can be used to provide a statistical assessment of the uncertainty of the estimated ratio-scales. In this paper we extend the results to a multi-level decision hierarchy commonly used in forest planning. We also provide a Bayesian extension of the regression technique. The advantage of the Bayesian approach is that it provides summaries of expert views that are easily understood by decision makers who may not have extensive understanding of statistical concepts. On the basis of the Bayesian analysis, one can calculate, for example, how likely it is that (in the view of the expert) a given forest plan is better than any other plan being compared. For. Sci. 43(4):521-528.