Behavioral Anomalies in Consumer Wait-or-Buy Decisions and Their Implications for Markdown Management
Bibliographic record
Abstract
The decision to buy an item at a regular price or wait for a possible markdown involves a multidimensional trade-off between the value of the item, the delay in getting it, the likelihood of getting it, and the magnitude of the price discount. Such trade-offs are prone to behavioral anomalies by which human decision makers deviate from the discounted expected utility model. We build an axiomatic preference model that accounts for three well-known anomalies and produces a parsimonious generalization of discounted expected utility. We then plug this behavioral model into a Stackelberg-Nash game between a firm that decides the price discount and a continuum of consumers who decide to wait or buy, anticipating other consumers’ decisions and the resultant likelihood of product availability. We solve the markdown management problem and contrast the results of our model with those under discounted expected utility. We analytically show that accounting for the behavioral anomalies can result in larger markdowns and higher revenue. Finally, we calibrate our model via a laboratory experiment and validate its predictions out-of-sample.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".