Optimal forest management under financial risk aversion with discounted Markov decision process models

The common assumption of risk-neutrality in forest decision making is generally inadequate since the stakeholders tend to be averse to fluctuations in the return criteria. In Markov decision process models (MDPs) of forest management, risk aversion and standard mean-variance analysis can be readily dealt with if the criteria are undiscounted expected values. However, with discounted criteria, such as the fundamental net present value of financial returns, the classic mean-variance optimization is numerically intractable. In lieu of this, this paper presents first a linear-programming method to calculate the variance of discounted criteria conditional on any specific policy. Second, it adopts as an alternative to the variance measure of risk, the â discount normalized varianceâ (DNV), an economically meaningful criterion consistent with income-smoothing behavior. The DNV is then used in procedures analog to mean-variance analysis and certainty-equivalent optimization tractable by quadratic programming. The methods are applied to the management of uneven-aged, mixed-species forests in the Southern United States. The results document the trade-off between the expected net present value and risk of financial returns, and the consequences for selected ecological criteria.

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