When Retailing and Las Vegas Meet: Probabilistic Free Price Promotions
Bibliographic record
Abstract
A number of retailers offer gambling- or lottery-type price promotions with a chance to receive one’s entire purchase for free. Although these retailers seem to share the intuition that probabilistic free price promotions are attractive to consumers, it is unclear how they compare to traditional sure price promotions of equal expected monetary value. We compared these two risky and sure price promotions for planned purchases across six experiments in the field and in the laboratory. Together, we found that consumers are not only more likely to purchase a product promoted with a probabilistic free discount over the same product promoted with a sure discount but that they are also likely to purchase more of it. This preference seems to be primarily due to a diminishing sensitivity to the prices. In addition, we find that the zero price effect, transaction cost, and novelty considerations are likely not implicated. This paper was accepted by Yuval Rottenstreich, judgment and decision making.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".