Sequential Solutions to Capacity‐Planning and Pricing Decisions*
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
Abstract Ideally, firms should jointly solve capacity‐planning and product‐pricing problems. In practice, informational limitations and cognitive bounds may force firms to sequentially solve the two problems. For example, a firm may plan capacity using limited demand information, and update prices subsequently once additional demand information becomes available. In a simple setting, we characterize the economic loss due to such sequential planning. We use simulation experiments to assess the extent of this loss in more complex settings. We find a relatively low loss if the firm plans for capacity using limited demand information and subsequently adjusts product prices to reflect realized market conditions. However, even “reasonable” restrictions on the subsequent price adjustment (e.g., constraining adjusted prices to always exceed full cost) lead to significant economic loss.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it