Coordination of quantity and shelf-retention timing in the video movie rental industry
Bibliographic record
Abstract
How should a video rental chain replenish its stock of new movies over time? Any such policy should consist of two key dimensions: (i) the number of copies purchased; and (ii) when to remove a movie from the front shelves and replace it by a newly released one. We first analyze this bi-variate problem for an integrated chain. As for decentralized chains, we show that a (wholesale) price-only contract cannot coordinate such a chain. We then consider a price-and-revenue-sharing contract. Such a contract can achieve coordination, but the unique price and share which are needed may not provide one of the parties with its desired profit (i.e., it will violate individual rationality). This situation has been reported in the case of Blockbuster Video and has led to litigation between Blockbuster and Disney Studios. We thus propose adding a third lever: a license fee (or subsidy) associated with each new movie. Such a contract can coordinate the channel and satisfy the individual rationality requirements. In fact, all our results hold true irrespective of whether or not the rental store is allowed to sell surplus copies of movies. We are able to compare the optimal decision variable and coordinating lever values, as well as the optimal profits, for the “rental only” and “sales + rental” models. Our numerical examples, which utilize empirical demand data have significant managerial implications in terms of increasing the effectiveness of the video rental industry.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".