<b>Case Article</b>—Bidding on Priceline
Bibliographic record
Abstract
Priceline.com is an Internet-based corporation offering services (airline tickets, hotel rooms, rental cars, and home mortgages) with the option for consumers to dictate prices. Priceline's original success stemmed from its innovative “name your price” approach whereby consumers bid for service with Priceline to find a willing provider at the bid price. Consumers are only allowed to bid once on a service so they must be strategic in bidding—bid too low and potentially lose out on service, bid too high and overpay for the service. While they are only allowed to bid once per product, bidders can slightly alter their service and potentially re-bid in an effort to get new information (from failed attempts)—such alterations may include a different class of hotel (3 star versus 4), a flight with 1 stop versus direct, or (as in the case below) a different class (economy versus luxury) of rental car. The case is designed to cover a broad range of topics while introducing students to auctions. The case teaches strategic bidding while covering probability, decision analysis, and integer programming. The case has had great success at the undergraduate and MBA levels as students enjoy the setting, are familiar with Priceline, and immediately see an everyday use of management science. Case Teaching Note: Interested Instructors please see the Instructor Materials page for access to the restricted materials. To maintain the integrity and usefulness of cases published in ITE, unapproved distribution of the case teaching notes and other restricted materials to any other party is prohibited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".