Behavior-Aware Queueing: The Finite-Buffer Setting with Many Strategic Servers
Bibliographic record
Abstract
In “Behavior-Aware Queueing: The Finite-Buffer Setting with Many Strategic Servers,” Zhong, Gopalakrishnan, and Ward develop a game-theoretic many-server Markovian queueing model with finite or infinite buffers to study the behavior of strategic servers whose choice of work speed depends on managerial decisions regarding (i) how many servers to staff and how much to pay them and (ii) whether and when to turn away customers. In order to predictably control system performance (e.g., lost demand, customer wait times, server burnout, etc.), they show that the system manager must either staff enough servers or pay them enough. For example, when servers are not paid enough, increasing their workload beyond a tipping point may result in a sharp drop in system performance because of server “rebellion.” Their work also establishes key foundational building blocks to advance the analysis of behavior-aware queueing models where both customers and servers are strategic and customers’ decisions endogenously induce a finite buffer.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".