Technical Note—A Risk- and Ambiguity-Averse Extension of the Max-Min Newsvendor Order Formula
Bibliographic record
Abstract
Scarf's max-min order formula for the risk-neutral and ambiguity-averse newsvendor problem is a classical result in the field of inventory management. In this article, we extend Scarf's formula by deriving an analogous closed-form order formula for the risk- and ambiguity-averse newsvendor problem. Specifically, we provide and analyze the newsvendor order quantity that maximizes the worst-case expected profit versus risk trade-off (risk-averse) when only the mean and standard deviation of the product's demand distribution are known (ambiguity-averse), and the risk is measured by the standard deviation of the newsvendor's profit. We provide both analytical and numerical results to illustrate the combined effect of considering risk aversion and ambiguity aversion in computing the newsvendor order.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".