Bibliographic record
Abstract
Money‐back guarantees (MBGs), which allow customers to return products that do not meet their expectations, are widely used in the retail industry. In this study, we study a retailer's MBG policy with dynamic pricing of limited inventory. A key decision for the retailer is to decide whether to offer MBGs. When the product can be returned instantly, we find that the optimal MBG policy is a simple threshold policy: given the inventory level, it is optimal to offer an MBG if and only if the remaining selling time is longer than a threshold. Moreover, the threshold is decreasing with the inventory level. We also address the problem of dynamic pricing with positive return times. Due to the complexity, we analyze the associated fluid model, which has an infinite number of constraints. We consider a series of relaxations that have a nested structure and use the Lagrangian approach to explicitly solve these relaxed problems. This allows us to develop an iterative approach that is guaranteed to solve the fluid model in finite iterations. Our numerical analysis shows that the deterministic solution is asymptotically optimal for the stochastic system.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".