Real options for precautionary fisheries management
Bibliographic record
Abstract
Abstract The 1996 Food and Agriculture Organization’s (FAO) ‘Guidelines on the Precautionary Approach to Fisheries and Species Introduction’ raise important issues for fisheries managers, but fail to prescribe an approach for risk management. The distinguishing characteristics of the ‘precautionary approach’ are the inclusion of uncertainty and ‘an elaboration on the burden of proof’. The FAO precautionary approach emphasizes that managers should be risk‐averse, but does not provide tools for determining the appropriate degree of risk aversion. Consequently, application of the precautionary approach often leads to decision‐making based on ad hoc safety margins. These safety margins are seldom chosen with explicit consideration of trade‐offs. If the emphasis was shifted to choosing between competing uncertainties, then managers could manage risk. By attempting to avoid risk, managers may gain exposure to other risks and perhaps miss valuable opportunities. We place fishery management problems within the rubric of ‘real investment’ problems, and compare and contrast the consideration of risk by alternative investment frameworks. We show that traditional investment frameworks are inappropriate for fishery management, and furthermore, that traditional precautionary approaches are arbitrary and without basis in decision theory. Quantitative decision‐making techniques, such as formal decision analysis (FDA), enable integration of competing hypotheses that help alleviate burden‐of‐proof issues. These techniques help analysts consider sources of uncertainty. FDA, however, can still be subject to arbitrary safety margins because such analyses often focus on determining which strategies best achieve, or avoid, targets that have been established without complete consideration of trade‐offs. A managerial finance approach, real options analysis (ROA), is an alternative and complementary decision‐making technique that enables managers to compute precautionary adjustments that couple the size of the ‘safety margin’ with the amount of uncertainty, thereby optimizing risk exposure and avoiding the need for arbitrary safety margins. We illustrate the advantages of an approach that combines FDA and ROA, using a heuristic example about a decision to re‐introduce Atlantic salmon ( Salmo salar L.) into Lake Ontario. Finally, we provide guidance on applying ROA to other fishery problems. The precautionary approach requires that managers consider risk, but considering risk is not the same as managing it. Here ROA is useful.
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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".