Behavior-Aware Queueing: The Finite-Buffer Setting with Many Strategic Servers
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it