Management of Complex Fisheries: Lessons Learned from a Simulation Model
Bibliographic record
Abstract
The purpose of this paper is to demonstrate how fisheries economics management issues or problems can be analyzed by using a complex model based on conventional bioeconomic theory. Complex simulation models contain a number of details that make them suitable for practical management advice, including taking into account the response of the fishermen to implemented management measures. To demonstrate the use of complex management models this paper assesses a number of second best management schemes against a first rank optimum (FRO), an ideal individual transferable quotas (ITQ) system. This is defined as the management scheme which produces the highest net present value over a 25 year period. The assessed management schemes (scenarios) are composed by several measures as used in the Common Fisheries Policy of the European Union for the cod fishery in the Baltic Sea. The scenarios are total allowable catches in combination with entry restrictions, and maximum number of days at sea in combination with entry restrictions. These two scenarios are assessed under assumptions of no cooperative behavior and cooperative behavior, and compliance and noncompliance with various management restrictions. Apart from showing the magnitude of the resource rent, the impact on fleet structure and the adjustment paths is shown. The result is that the resource rent gained from these second best management schemes is lower than FRO, the ideal ITQ system, but may in practice not be so different.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".