Play It Again, Sam? Optimal Replacement Policies for a Motion Picture Exhibitor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Every week, motion picture exhibitors must decide whether to keep or replace the movies playing in their theaters in light of the past week's sales data. This decision is complex because of the dynamic decision environment, the uncertainty of demand, the complex revenue-sharing terms between the retailer and the distributor, the need to commit to new movies for several weeks, and the competitive release patterns of movies. We formulate this problem as a Markov Decision Process (MDP) model, using it to obtain replacement policies for the exhibitor. We examine the effect of differences in quality and quantity of available movies, and their respective release dates on the returns from model-based normative solutions. We also show that two practical heuristics are significantly outperformed by the optimal policy for the MDP model. We conclude by applying the model to industry data.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it