On solving the multirotational timber harvest problem with stochastic prices: A linear complementarity formulation
Bibliographic record
Abstract
This article develops a two-factor real options model of the harvesting decision over infinite rotations assuming a known stochastic price process and using a rigorous Hamilton–Jacobi–Bellman method-ology. The harvesting problem is formulated as a linear complementarity problem that is solved nu-merically using a fully implicit finite difference method. This approach is contrasted with the Markov decision process models commonly used in the literature. The model is used to estimate the value of a representative stand in Ontario’s boreal forest, both when there is complete flexibility regarding harvesting time and when regulations dictate the harvesting date. Key words: linear complementarity problem, Markov decision process, mean reversion, optimal har-vesting, real options. The forest economics literature has long dealt with the problem of optimal harvesting under uncertainty. An overview is provided in re-cent bibliographies by Newman, and Brazee and Newman. A thirty-year-long strand of this literature emphasizes the importance of valuing managerial flexibility in the context of irreversible harvesting decisions when for-est product prices are volatile relative to har-vesting costs (Hool; Lembersky and Johnson). Failure to include the value of management options where they exist will result in an in-correct valuation of a forestry investment. Because the formulation and modeling of tim-ber harvesting problems to accurately incor-porate the value of managerial flexibility is unlikely to result in closed-form solutions, for-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".