Green Simulation with Database Monte Carlo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available for further experimentation before the next question is asked. The general strategy is database Monte Carlo for green simulation: the output of experiments is stored in a database and used to improve the computational efficiency of future experiments. In this article, the database provides a quasi-control variate, which reduces the variance of the estimated mean response in a future experiment that has a fixed computational budget. We propose a particular green simulation procedure using quasi-control variates, addressing practical issues such as experiment design, and analyze its theoretical properties. We show that, under some conditions, the variance of the estimated mean response in an experiment with a fixed computational budget drops to zero over a sequence of repeated experiments, as more and more idle time is invested in creating databases. Our numerical experiments on the procedure show that using idle time to create databases of simulation output provides variance reduction immediately, and that the variance reduction grows over time in a way that is consistent with the convergence analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it