Consideration of uncertainty in forest management decision-making Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/2b88qh53f

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  • Management decisions are generally considered to be made under one of three categories of future knowledge: certainty, risk, or uncertainty. All three categories occur in forest management. However, forest management decisions whose outcomes are dependent upon future levels of timber yields, prices, utilization standards, or social and legal institutions are made under uncertainty. Forest managers have always recognized that uncertainty existed; however, they have not systematically included it in their decision- making process. The objectives of the study were to: (1) establish the importance of systematically considering uncertainty in forest management decision-making and (2) illustrate and evaluate a model or procedure, for the systematic consideration of uncertainty in forest management decision-making. A review of the present status of forest management decision-making constituted fulfillment of the first objective. Theoretical decision-making models which are currently used in forest management, e. g., present worth analysis, capital budgeting, financial maturity, and linear programming, while conceptually capable of considering uncertainty, imply certainty That is, forestry applications of these models have employed single-valued expectations. Fulfillment of the second objective consisted initially of a review of recent developments in the theory of decision-making under uncertainty. All decision-making problems have some common components. These components are: decision-alternatives, the actions which the decision-maker deems possible to take; states of nature, the future events which determine the outcome of the actions; and consequences, the result of taking a specific action and finding that a particular state occurs. The more popular theoretical models for decision-making under uncertainty were reviewed: minimax, minimax regret, Hurwicz index, and Laplace. While useful in some cases, each of these models has specific disadvantages. In addition, all the models have one common major disadvantage, they contain the implicit assumption that the decision-maker is completely ignorant about the states of nature which influence his problem. In reality, forest managers and other decision-makers usually possess some information, although it may be vague, about their problems. If a decision-maker is not willing to assume complete ignorance about the occurrence of the states of nature, he cannot apply any of the above models. There is a theoretical decision-making model which appears compatible with reality. The model, Bayesian decision theory, allows the decision-maker to arrive at a solution which is compatible with his opinions or judgements about the states of nature. Also, he can combine these opinions or judgements with experimental data to derive a solution using all available information, both subjective and objective. Fulfillment of the second objective was completed by illustrating the application of Bayesian decision theory to a hypothetical problem. The problem, optimal degree of land ownership for an industrial forestry firm, was defined within the Bayesian model and a solution derived. Since the problem was hypothetical, the actual solution is not the primary result of the study. The resulting implications for actual situations is the primary contribution. If forest managers are to make decisions which contain uncertainty, the uncertainty should be systematically recognized in the decision-making process. The Bayesian model is a logical procedure for such recognition. By adopting and applying such models, the efficiency of forest management decision-making will be increased.
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