Coupons and ‘everyday low prices’: price competition with multiple instruments
Bibliographic record
Abstract
Abstract A simple two‐stage game is examined, where firms compete in prices by chosen pricing instruments. Those considered include a simple, uniform pricing technology and a promotional pricing technology like an advertised discount coupon. Consumers are separated by types, informed and uninformed. Therefore, a motive for price competition exists for the purpose of separating the two types of consumers. It is shown that the sustainability of an asymmetric choice of pricing instrument between the two firms will prevail in a duopoly market in equilibrium. Consequently, the coexistence of two different pricing schemes is viable even when firms are otherwise symmetric. JEL Classification: L1, L13 Coupons et « bas prix tous les jours » : concurrence par les prix à l’aide de multiples instruments Ce mémoire examine un simple jeu à deux étapes dans lequel des entreprises se concurrencent par les prix à l’aide d’un instrument particulier. Les instruments considérés incluent une simple technologie de prix uniforme, et une technologie de prix de promotion sous la forme de l’annonce d’un coupon d’escompte. On divise les consommateurs en deux groupes – ceux qui sont informés et ceux qui ne le sont pas. Un des motifs pour ce genre de concurrence est de séparer les deux types de consommateurs. On montre qu’on peut soutenir un choix asymétrique d’instrument de tarification entre les deux entreprises dans un marché de duopole en équilibre. En conséquence, la coexistence de deux stratégies de prix différentes est viable quand les entreprises sont symétriques sous tous les autres rapports.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".