Using history matching to speed up management strategy evaluation grid searches
Bibliographic record
Abstract
Management strategy evaluations (MSEs) are a valuable tool for assessing the performance of different management strategies under varying ecological and economic conditions. They can be used to optimise the management procedure. However, MSEs are computationally demanding, especially as the complexity of operating models increases. I propose a scheme based on history matching to speed up grid searches when looking to optimise control parameters in harvest control rules. The approach uses an emulator, a fast statistical model that mimics the MSE, to exclude points that have a high probability of not being the optimal. The emulator is updated until only one point remains. The methods are introduced and demonstrated using an MSE on North Sea cod. Typically, the method found the optimal solution with only 9% of the grid evaluated in 12% of the clock time. I compared history matching with alternative optimisation algorithms, including hill climbing, simulated annealing and the genetic algorithm, and found that history matching consistently outperformed the alternatives.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".