Efficient Inaccuracy: User-Generated Information Sharing in a Queue
Bibliographic record
Abstract
We study a service system that does not have the capability of monitoring and disclosing its real-time congestion level. However, the customers can observe and post their observations online, and future arrivals can take into account such user-generated information when deciding whether to go to the service facility. We perform pairwise comparisons of the shared, full, and no queue-length information structures in terms of social welfare. Perhaps surprisingly, we show that the shared queue-length information may provide greater social welfare than full queue-length information when the hassle cost of the customers entering the service facility falls into some ranges, and the shared and full queue-length information structures always generate greater social welfare than no queue-length information. Therefore, the discrete disclosure of congestion through user-generated sharing can lead to as much, or even greater, social welfare as the continuous stream of real-time queue-length information disclosure and always generates greater social welfare than no queue-length information disclosure at all. These results imply that a little shared queue-length information—inaccurate and lagged—can go a long way and that it may be more socially beneficial to encourage the sharing of user-generated information among customers than to provide them with full real-time queue-length information. This paper was accepted by Terry Taylor, operations management.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".