Centralized and Decentralized Approaches to Water Demand Management
Bibliographic record
Abstract
Centralized and decentralized procedures to assess the impacts of water demand management on a water system and its users are investigated and compared. Within the centralized approach, a system-wide optimization technique is firstly utilized to estimate the overall optimal net benefits when the water demand management initiatives are implemented. Cooperative game-theoretic methods are used to fairly redistribute the additional net benefits. In terms of the decentralized perspective, an agent-based modelling framework is adopted to permit each user to make independent decisions on whether to conserve water or consume extra water and how much to conserve or consume by solving individual optimization problems. For comparison purposes, both the centralized and decentralized approaches are applied to a case study reflecting an actual situation in the South Saskatchewan River basin in Alberta, Canada. Both methods provide positive incentives to encourage users to conserve water while maintaining at least the same level of economic benefits such that system-wide productivity is improved. Moreover, the study demonstrates that the centralized method produces greater overall net benefits, but the users may be less motivated to participate.
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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".