On Customer (Dis)honesty in Priority Queues: The Role of Lying Aversion
Bibliographic record
Abstract
Queues where people misreport their private information to access service faster are everywhere. Motivated by \nthe prevalence of such behaviour in practice, we construct a queueing-game-theoretic model where customers \nmake strategic claims to reduce their waiting time, and the Manager decides on the static scheduling policy \nbased on those claims to minimize the expected delay cost in the system. We develop a lying aversion model \nwhere customers incur both delay and lying costs. We run controlled experiments to validate our modelling \nassumptions regarding customer misreporting behaviour. In particular, we find that people do incur lying \ncosts, and that their misreporting behaviour does not depend on changes in waiting times, but rather on the \nscheduling parameters. Based on the validated lying aversion model, we study the equilibrium that arises \nin our game. We find that under certain conditions, the optimal policy is to use an honor policy where \nservice priority is given according to customer claims. We also find that it may be optimal to incentivize \nmore honesty by means of an upgrading policy where some customers who claim to not deserve priority are \nupgraded to the priority queue. We find that the upgrading policy deviates from the celebrated cµ rule.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".