Optimal harvest with multiple fishing zones, endogenous price and global uncertainty
Bibliographic record
Abstract
The literature on the optimal fish harvest has concentrated on a single fishery facing multiple sources of uncertainty. In this paper we develop and implement a stochastic optimal control approach to determine the value-maximizing harvest of a fishery participating in a global market, where multiple harvesting zones sell their production. The global market is characterized by an inverse demand function, which combines a stochastic exogenous demand factor and the aggregate harvesting of all zones. Accordingly, a fishery's optimal harvest will be affected by global demand shocks and the harvesting in all the competing zones, through the global price. We consider two sources of uncertainty for the biomass, local and global biomass shocks. Through global biomass shocks, the model provides enough flexibility to incorporate the correlation between biomass shocks in multiple zones. To illustrate the implementation of the approach we apply it to the Alaska and British Columbia halibut fishery. When we compare our global competitive framework with an alternative where all zones are aggregated into a single monopolistic fishery, we find that, for the estimated parameters, competition will increase the optimal global harvest and consequently reduce the fish price without affecting the sustainability of the resource. This illustration shows that a regulator fixing total annual fish catch needs to take into consideration the structure of the market (monopolistic versus competitive) to determine the optimal level of the quotas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".