Optimal Harvest with Multiple Fishing Zones, Endogenous Price and Global Uncertainty
Bibliographic record
Abstract
The literature on the optimal harvest of fisheries has concentrated on a single fishing area with biomass uncertainty and to a lesser degree also with price uncertainty.We develop and implement a stochastic optimal control approach to determine the harvest that maximizes the value of a fishery participating in a global market, where all the considered harvesting zones sell their production.This market is characterized by an inverse demand function, which combines an exogenous demand shock and the aggregate harvesting of all zones.Accordingly, a fishery's harvest will be affected by the global demand shocks and the harvesting in all the competing zones through the global selling price.In addition, we decompose the biomass uncertainty into local and global biomass shocks.Through global biomass shocks, the model provides enough flexibility to acknowledge for correlation in the biomass shocks faced by the multiple perhaps adjacent areas.When we compare our global framework with an alternative where the individual zones are aggregated into a single optimizing fishery we find that competition will increase the global harvest and consequently reduced the resource price.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".