Numerical-Based Approach for Updating Simulation Input in Real Time
Why this work is in the frame
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Bibliographic record
Abstract
Simulation has assisted engineers in various decision-making processes for decades. Particularly, modeling inputs as probabilistic distributions enables these stochastic models to capture uncertainties and represent random processes. A significant number of studies have developed an accurate input model from a single source type (i.e., quantitative observations or subjective information), but few have integrated multiple information sources dynamically. Nevertheless, the latter situation is common in construction projects, especially during project execution when quantitative observations and expert opinions need to be factored into models in real time. This paper is the first to propose coupling a Markov chain Monte Carlo (MCMC)–based numerical method with a weighted geometric average (GA) as a novel approach to systematically update inputs for stochastic simulation models. The proposed method handles both objective and subjective project data to effectively update the input models in real time, producing more accurate representations of probabilistic input models for any Monte Carlo (MC)–driven simulation. This method considerably improves the reliability, accuracy, and practicality of stochastic simulation models.
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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