Multi-agent systems for collective management of a northern Thailand watershed : model abstraction and design
Bibliographic record
Abstract
Scarce farmland and water resources in the highland watersheds of northern Thailand coupled with multiple users and desires have led to conflicts among stakeholders who play important roles in the system dynamics. Integrating the participatory approach and multi-agent systems (MAS) modeling can facilitate adaptive learning processes to result in a collective management strategy that meets the balanced needs of all parties. However, this requires participation and cooperation from all stakeholders involved in the process. This paper elaborates on the concept and processes of MAS model abstraction and design, which is the first study period of a research project. This project aims at developing an integrated participatory MAS model to support collective resource management in a watershed area of northern Thailand. A prototype MAS model was constructed using unified modeling language (UML) diagrams, and it consists of three major components: a biophysical module, a social module, and a political institution module. UML diagrams are used as an interface for discussing and sharing ideas among an interdisciplinary research team. This prototype structure will be used as a guideline for further research steps, including stakeholder analysis, eliciting common representations for further model programming, and development. Finally and hopefully, the verified and validated MAS model could be applied in assessing natural resource management strategies agreed upon among all stakeholders and would result in the implementation of a desired intervention scheme for sustainable resource management in this watershed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".