Factors affecting warm-up periods in discrete event simulation
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 this paper, we discuss the factors affecting the initialization bias in discrete event simulation. Specifically, we assume that the time average is used to find the equilibrium expectation of a certain variable [Formula: see text], say the number in a queueing network, and we would like to minimize the mean squared error (MSE) between the time average of [Formula: see text] and its equilibrium expectation. To do this, a warm-up period is often used during which no data is collected, and we want to find the length of this period such that the MSE is minimal. We show that if starting in what Tocher calls a “typical condition”, warm-ups tend to be redundant. This result is strengthened by theoretical arguments and numerical experiments. If starting in a typical state is inconvenient, warm-up periods should be used, and methods to find optimal warm-up periods are discussed. The numerical methods used for our experiments do not rely on Monte Carlo simulation. Instead, we determine the MSE of the time average by the randomization method and other deterministic methods.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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