A game theoretic decision-making approach to reduce mine closure risks throughout the mine-life cycle
Bibliographic record
Abstract
To align a post-mining site to the wants and needs of local communities and stakeholders, mine closure needs to consider criteria which may be difficult to value using conventional cost–benefit analysis. For example, criteria associated with the environment, socio-environmental relationships, health, and long-term sustainability. Game theory techniques are implemented to investigate how different stakeholder groups could act during mine closure planning and decision-making. Using game theory with sustainability criteria, this study proposes an innovative approach to analyze some key decisions throughout the mine life that affect closure. Non-cooperative game theory models are investigated by developing Nash equilibrium equations which are based on the change in environmental risk, economic potential, and impacts on company reputation. The modified equilibrium formulas can highlight the key sustainability criteria for multi-stakeholder closure planning and complex decision-making. The discussion explores how the developed model can assist mining stakeholders in understanding their position during the mine closure process. In all, the game theory and multiple criteria models can help structure and manage the complex closure risks throughout the mine life cycle.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".