Dynamic Pricing Through Discounts for Optimizing Multiple-Class Demand Fulfillment
Why this work is in the frame
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Bibliographic record
Abstract
In a multiple-customer-class model of demand fulfillment for a single item, we consider the use of dynamic price discounts to encourage backlogging of demand for customer classes denied immediate service. Customers are assumed to arrive over several stages in a period, and customer classes are distinguished by their contractual price and sensitivity to discounts. Through dynamic programming we determine the optimal discounts to offer, assuming a linear model for the sensitivity of customers to such inducements. We show that customers are served in class order, and allocation of inventory to demand is determined by considering the current number of customers backlogged, as well as the current inventory position. Through comparison to a naive supplier allocating inventory first come/first served with no discounting, we show that profits are primarily influenced by the allocation of capacity, and the use of price discounts primarily benefits the second-class customers’ overall fill rate. Heuristics for implementation of the solution in real-time settings are given.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it