Tight Bounds on the Competitive Ratio on Accommodating Sequences for the Seat Reservation Problem
Bibliographic record
Abstract
The unit price seat reservation problem is investigated. The seat reservation problem is the problem of assigning seat numbers on-line to requests for reservations in a train traveling through k stations. We Computer Sciences Department, University of Wisconsin { Madison, 1210 West Dayton Street, Madison, WI 53706-1685, U.S.A. Email: bach@cs.wisc.edu. y Department of Mathematics and Computer Science, University of Southern Denmark, Main Campus: Odense University, Campusvej 55, DK-5230 Odense M, Denmark. Email: fjoan,lenem,kslarseng@imada.sdu.dk. z School of Computer and Media Sciences, The Interdisciplinary Center, P.O.B. 167, 46150 Herzliya, Israel. Email: epstein.leah@idc.ac.il. x Department of Computer Science, University of California, Riverside. On leave from Department of Computing and Software, McMaster University, Hamilton, Ontario L8S 4L7, Canada. Email: jiang@cs.ucr.edu. { Department of Computer Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada and Department of Computing and Software, McMaster University, Canada. Email: ghlin@wh.math.uwaterloo.ca. k Centre for Mathematics and Computer Science (CWI), P.O.B. 94079, 1090 GB Amsterdam, The Netherlands. Email: Rob.van.Stee@cwi.nl. 1 are considering the version where all tickets have the same price and where requests are treated fairly, i.e., a request which can be fullled must be granted. For fair deterministic algorithms, we provide an asymptotically matching upper bound to the existing lower bound which states that all fair algorithms for this problem are 1 2 -competitive on accommodating sequences, when there are at least three seats. Additionally, we give an asymptotic upper bound of 7 9 for fair randomized algorithms against oblivious adversaries. We also examine concrete ...
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".