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Real options for precautionary fisheries management

2008· article· en· W2110424412 on OpenAlexaboutno aff
Eli P. Fenichel, Jean I. Tsao, Michael L. Jones, Graham J. Hickling

Bibliographic record

VenueFish and Fisheries · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State UniversityGreat Lakes Fishery Commission
KeywordsPrecautionary principleRisk analysis (engineering)Risk managementRubricFisheries managementBusinessInvestment (military)Risk aversion (psychology)EconomicsActuarial scienceExpected utility hypothesisFisheryFishingFinanceFinancial economics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.205
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2008
Admission routes1
Has abstractyes

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