On the risk of systematic drift under incoherent hierarchical forest management planning
Bibliographic record
Abstract
In theory, linkages between hierarchical forest management planning levels ensure coherent disaggregation of long-term wood supply allocation as input for short-term demand-driven harvest planning. In practice, these linkages may be ineffective, and solutions produced may be incoherent in terms of volume and value-creation potential of harvested timber. Systematic incoherence between planned and implemented forest management activities may induce drift of forest system state (i.e., divergence of planned and actual system state trajectories), thus compromising credibility and performance of the forest management planning process. We describe hierarchical forest management from a game-theoretic perspective and present an iterative two-phase model simulating interaction between long- and short-term planning processes. Using an illustrative case study, we confirm the existence of a systematic drift effect, which we attribute to ineffective linkages between long- and short-term planning. In several simulated scenarios, the planning process fails to ensure long-term wood supply sustainability, fails to reliably meet industrial fiber demand over time, and exacerbates incoherence between wood supply and fiber demand over several planning iterations. We show that manipulating linkages between long- and short-term planning processes can reduce incoherence and describe future work on game-theoretic planning process model formulations that may improve hierarchical planning process performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".