Governing Common Pool Resources in Fragile Political Systems: Modelling Behaviour, Institutions, and Social-Ecological Dynamics
Bibliographic record
Abstract
Groundwater user groups in Tunisia face severe collective action problems. Aquifer depletion leads to empty wells and farmers’ unwillingness to pay water fees leads to bankrupt user groups – both disastrous for the many communities that rely on irrigation agriculture for their livelihoods. What conditions or combination of conditions drive water user behaviour in a system that is governed by institutional uncertainty and bounded rationality? What conditions or interventions are effective in avoiding or delaying system collapse? What is the role of social norms, particularly trust and leadership, in overcoming collective action problems? Based on and expanding on the theory of common pool resource governance, this paper ties institutional results to environmental outcomes. The complex common-pool resource system studied here is simulated by an Agent-Based Model (ABM) of groundwater user decision-making. This systematic coupling of social and biophysical data and models offers new insights into simulating dynamic interactions between human behaviour, social norms, and the underlying resource. The project aims to provide a guideline for alternative modes of policy-making and implementation to address the main water governance challenge in Tunisia, i.e. groundwater overexploitation.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".