Governance in social-ecological agent-based models: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analyzing governance is particularly important for understanding and managing social-ecological systems (SES).Governance systems influence interactions between actors and the ecological system and are in turn influenced by the changes that occur in the actors' and ecological systems.Agent-based models (ABM) are well adapted for studying SES, for exploring interactions and the resulting collective behavior and for predicting the results of management processes.Considering the potential of ABM to analyze SES, we performed a literature review of the modeling of governance in ABM of SES and highlight the perspectives and challenges surrounding this issue.Our results show in particular that a significant share of the literature is not explicitly based on theories supporting the modeling of governance and actors' decision making.Regarding the conceptualization of governance, formal and informal institutions are rarely represented compared with diverse modes of governance.The governance modes that are mostly modeled are state interventions whereas the community-based and market-based modes of governance are scarcely represented.Finally, the overview of how interactions between governance and SES are operationalized in ABM highlights two main forms of implementation of governance: variable-based and agent-based implementations.The corresponding sets of models differ in terms of main theoretical background, types of governance modes represented or presence of interactions.Therefore, we recommend moving toward a greater diversity in the representation of governance and toward a better implementation of the dynamics of models, which can be facilitated by the explicit use of theories supporting the modeling of governance and the decision making of actors and by the representation of governance as an agent.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it