Governance in social-ecological agent-based models: a review
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".