Optimal Price Setting With Observation and Menu Costs
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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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 | 0.011 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".