Modelling to Generate Alternative Policies in Highly Uncertain Environments: An Application to Municipal Solid Waste Management Planning
Bibliographic record
Abstract
Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to quantify and capture at the time supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable degrees of stochastic uncertainty. Furthermore, there are frequently numerous stakeholders with incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the construction of a decision support model, which can greatly impact the acceptability of its solutions. While a mathematically optimal solution may be the best solution to the modelled problem, it is frequently not the best solution to the real, underlying problem. Therefore, in public environmental policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the problem. By generating a diverse set of solutions, it is hoped that some of these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be used to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new approach is very computationally efficient, since, in addition to finding the best solution to the problem, it permits the simultaneous generation of multiple, good solution alternatives in a single computational run of the SO algorithm rather than the multiple implementations required in other modelling-to-generate-alternatives procedures. The efficacy of this approach is specifically demonstrated using a previously studied waste management case from the Municipality of Hamilton-Wentworth, Ontario.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".