Optimal Price Setting With Observation and Menu Costs
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
We study the price-setting problem of a firm in the presence of both observation and menu costs. The firm optimally decides when to “review” costly information on the adequacy of its price. Upon each review, the firm chooses whether to adjust its price, one or more times, before the next price review. Each price adjustment entails paying a menu cost. The firm's choices map into several statistics: the frequency of price reviews, the frequency of price adjustments, the size distribution of price changes, and the hazard rate of price adjustments. The simultaneous presence of observation and menu costs produces complementarities that change the predictions of simpler models featuring one cost only. For instance, infrequent observations may reflect a high menu cost rather than high observation costs: in spite of these complementarities, we show that the ratio of the two costs is identified by several statistics on price observations and adjustments.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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