Analysis of fish harvesting decisions under parametric and structural uncertainty
Bibliographic record
Abstract
Canada has had a long involvement with the fishing industry. Despite this experience, however, fishery management is still full of uncertainty. The fish, living fully in an aquatic environment, cannot be observed ' in situ'. Also, humans are involved. Traditionally, economists have used a bi-partite classification of uncertainty---splitting the possibilities into risk and uncertainty. Recently, Langlois (1984, 1994) has clarified this distinction and relabeled the categories as parametric uncertainty and structural uncertainty. This thesis identifies the characteristics which help to identify a particular situation as involving parametric or structural uncertainty. It then evaluates four models of choice---Expected Utility Theory, Cumulative Prospect Theory, Real Options Theory and Shackle's Theory of Investment Decisions in the context of the two types of uncertainty. This evaluation is done through two lenses. The first evaluation is methodological, comparing the four models with the logical requirements of logical positivism, Austrian a priorism, scientific realism and instrumentalism. The second evaluation compares the first and last of the decision models in a simulated fishery management situation based on the lake whitefish fishery of Lake Huron in Ontario. The evaluation confirms that the type of uncertainty encountered makes a clear analytical difference and that the models predict different outcomes under each type of uncertainty.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".