Bibliographic record
Abstract
Simple selling mechanisms are very appealing in practice, yet they may limit the firm’s ability to extract a large consumer surplus. In this paper, we study the efficiency of a simple pure bundling mechanism in extracting the consumer surplus in the presence of nonnegative marginal costs and correlated valuations. The main question we address is how to quantify the benefits of adopting a simple pure bundling mechanism relative to other more complicated mechanisms, such as mixed bundling. We develop simple robust analytics that identify the main drivers for the efficiency of the pure bundling mechanism and allow the sellers to easily quantify the potential profit of a large‐scale bundling mechanism relative to more complicated selling mechanisms. Our numerical simulations show that these analytics provide high predictive power for the true performance of the bundling mechanism and are robust to different parametric assumptions even for relatively small bundles. For example, for a bundle of 1200 and 12,000 items, the medians of the prediction error of our tight bound are −0.021 and 0.041 while the interquartile ranges are 0.094 and 0.067, respectively.
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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 | 0.008 | 0.052 |
| 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 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".