Markdown Pricing with Unknown Fraction of Strategic Customers
Bibliographic record
Abstract
A growing segment of the revenue management and pricing literature assumes “strategic” customers who are forward-looking in their pursuit of utility. Recognizing that such behavior may not be directly observable by a seller, we examine the implications of seller uncertainty over strategic customer behavior in a markdown pricing setting. We assume that some proportion of customers purchase impulsively in the first period if the price is below their willingness to pay, while other customers strategically wait for lower prices in the second period. We consider a two-period selling season in which the seller knows the aggregate demand curve but not the proportion of customers behaving strategically. We show that a robust pricing policy that requires no knowledge of the extent of strategic behavior performs remarkably well. We extend our model to a setting with stochastic demand, and show that the robust pricing policy continues to perform well, particularly as capacity is loosened or the problem is scaled up. Our results underscore the need to recognize strategic behavior, but also suggest that in many cases effective performance is possible without precise knowledge of strategic behavior.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".