Strategic Behavior and Social Outcomes in a Bottleneck Queue: Experimental Evidence
Bibliographic record
Abstract
We consider a class of three-player queuing games where players independently choose when to arrive at a bottleneck facility that serves only one at a time. Players are impatient for service but cannot arrive before the facility opens and they dislike time spent in queue. We derive the equilibrium arrivals under the first-in-first-out (FIFO), last-in-first-out (LIFO), and service-in-random-order (SIRO) queue disciplines and compare these equilibrium predictions to outcomes from a laboratory experiment. LIFO provides higher equilibrium welfare than FIFO and SIRO since the players arrive such that lower congestion is induced. Experimental evidence confirms that employing different queue disciplines indeed affects the strategic behavior of players and thereby the level of congestion. The experimental participants do not, however, behave as prescribed by the equilibrium predictions. They obtain significantly higher welfare than prescribed by equilibrium under all queue disciplines. Our results moreover suggest that people perceive LIFO as the most unfair of the three disciplines although the theoretical results suggest that it is welfare optimal.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".