Shifting strategies: exploring cooperation dynamics in fisheries co-management
Bibliographic record
Abstract
In the governance of common-pool resources (CPRs), co-management policies are a conventional approach to prevent the tragedy of the commons. Although generally efficient, the performance of these policies varies across communities. Experimental economics applied to co-management settings has widely informed this heterogeneity. However, progress made in experimental economics in understanding how cooperative strategies influence cooperation dynamics has seldom been applied to explain the diverse outcomes observed in co-management. Investigating how cooperative strategies are shaped by institutions within co-management schemes can deepen our understanding of the behavioral mechanisms and motivations driving resource users’ actions to better inform co-management policies. We propose that variation in co-management performance can be explained by the distribution of strategic types within user groups and how these distributions shift in response to external enforcement, a common institution in co-management policies. Employing a repeated common pool resource game experiment, we investigated small-scale fishing communities in Chile that had been previously categorized in types of user groups based on their real-life experience with co-management (no experience, high performance, and lower performance). In the experiment, all subjects participated in two treatments: one without enforcement of a social norm and one with a non-deterrent external enforcement of the social norm (resembling the actual co-management institution faced by the experimental subjects). We then classified fishers' cooperative strategies in each treatment as either free-riders, conditional cooperators, unconditional cooperators, or negative cooperators, and assessed the distribution of strategies across types of user groups in both treatments. We found that strategic heterogeneity can explain differences in co-management outcomes only under external enforcement. These results underscore differences in how user groups develop cooperative norms to sustain common-pool resources, and suggest that external enforcement helps signaling these norms, preventing the erosion of cooperation through shifts in strategies. This process reveals underlying behavioral mechanisms and motivations that influence users involved in co-management and should be considered to foster the sustainable use of natural resources.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".