Anticipating adaptation: a mechanistic approach for linking policy and stock status to recreational angler behavior
Bibliographic record
Abstract
We use techniques from economic recreation demand modeling to develop a mechanistic model of individual recreational fishing behavior and estimate it using license-frame survey data. By consistently integrating individuals’ seasonal decisions of where, whether, and how much to fish, the model generates predictions of aggregate indicators such as angler-days and fishing mortality as phenomena arising from individual behavior. We use the model to simulate alternative future scenarios by altering policy variables or measures of fishing quality, such as catch rates. The mechanistic nature of the model incorporates anglers’ adaptive behavior to these stimuli, generating scenarios that are likely more robust to shifts in the decision context than many commonly used phenomenological models. We utilize the model to examine the sensitivity of total catch and catch per unit effort (CPUE) to changes in fish stocks, revealing substantial nonlinearities in this relationship. We also simulate total fishing trips, participation, CPUE, and total catch for a seasonal fishing permit versus a per-trip fee, finding dramatic differences across the two policies that call into question the wisdom of permit fees as management tools.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".