Adaptive Decision Analysis in Forest Management Planning

Abstract Societal responsibilities of forestry have diversified, and criteria other than those related to wood production have been given more weight. Demands for forest management planning methods have changed in line with the changes in the operational environment. Thus, forest planners are facing a growing demand for more versatile and customer-oriented decision aid systems and practices in which the data are cost effectively acquired and used. So far, forest planning research has concentrated mainly on the general development of multiobjective and participatory planning methods in the numerical framework. This focus has unintentionally led to practices for which the decision analysis process is often controlled only by the technical needs of the planning methods in a unified setting, independently of the problem characteristics. However, it is important to adapt the use of planning methodologies to each planning problem and use the available information about important variables efficiently. In this article, we introduce a theoretical and methodological framework for adaptive decision analysis (ADA) for forest management planning. In ADA, different decision-support service options, comprising both qualitative and quantitative components, are maintained and carried out flexibly in each planning case with the aid of an adapting processor and feedback management, resulting in computational, social, and organizational learning.