Queueing Causal Models: Comparative Analytics in Queueing Systems
Why this work is in the frame
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Bibliographic record
Abstract
Problem definition: Much of the focus of queueing theory (QT) is on performance evaluation that supports comparative analytics—that is, comparing performance measures under different interventions. However, closed-form queueing models are very sensitive to assumptions. We develop a data-driven Structural Causal Queueing Model (SCQM)—a form of structural causal models that automatically adapts to the data-generating process of queueing systems, finds causal relations, and supports comparative analytics. Numerical experiments show that the accuracy of SCQM is competitive with QT, even for examples where analytical queueing solutions are available. Methodology: We employ structural causal modeling methodology that uses queueing-relevant features to develop a simulator that replicates the system’s data-generating process without requiring prior knowledge of its dynamics. We apply Machine Learning models for identifying the parent sets and causal relations. We then provide intervention analysis using Monte Carlo simulation. Managerial implications: We use queueing knowledge to develop an accurate self-adapting data-driven performance evaluator for congested systems that requires no prior knowledge of the system dynamics. Using this method, companies can perform comparative analytics of interventions for queueing systems that may not be analytically solvable. History: This paper was selected as part of the 1RR initiative between M&SOM and the MSOM Society. This paper was part of the 2024 MSOM Service Operations Service Management Special Interest Group Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1515 .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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