Optimal forest management under financial risk aversion with discounted Markov decision process models
Bibliographic record
Abstract
The common assumption of risk neutrality in forest decision making is generally inadequate because the stakeholders tend to be averse to fluctuations in the return criteria. In Markov decision processes (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 (i) presents a linear-programming method to calculate the variance of discounted criteria conditional on any specific policy and (ii) adopts, as an alternative to the variance measure of risk, the “discount normalized variance” (DNV), an economically meaningful criterion consistent with income-smoothing behavior. The DNV is then used in procedures analogous 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, as well as the consequences for selected ecological criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".