Lightening the performance burden of individual‐based models through dimensional analysis and scale modeling
Bibliographic record
Abstract
Abstract While individual‐based models are attractive for some modeling problems, the lengthy times required for simulating large populations can impose high opportunity costs by limiting model comprehension, refinement and user interaction. This paper demonstrates a novel technique for using dimensional analysis and scale modeling to reduce the performance barriers associated with individual‐based model simulation. Given a dimensionally homogeneous simulation model with a large population, we propose a rigorous, systematic and general‐purpose technique to formulate a “reduced‐scale” individual‐based model that simulates a smaller population. Outputs of the reduced‐scale models can be precisely transformed to yield results representative of a full‐scale model—without the need to run the full‐scale model. While discretization effects and heterogeneity limit the degree of scaling that can be achieved, these techniques are notable in relying only upon dimensional homogeneity of the full‐scale model, and not on the specifics of model behavior or use of a particular mathematical framework. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".