Newsvendor Game: A Behavioral Exercise in Decision Making under Risk
Bibliographic record
Abstract
ABSTRACT The newsvendor problem is a classic problem of decision making under risk that is taught in traditional Operations and Supply Chain Management classes as a single‐period inventory problem. We discuss the following three pedagogical points of interest to any instructor tasked with teaching this topic: a) why the newsvendor model is relevant in this day and age; b) how to make better sense of the newsvendor problem for students; and c) how to easily implement and administer an active learning exercise in either a traditional classroom, or an online setting. This active learning exercise is a quick, effective, and meaningful way of demonstrating a variety of concepts related to the newsvendor problem that include: a) the rational economic method of calculating optimal order quantity, b) the inherent risk in forecasting and ordering decisions as they relate to surpluses and shortages; and c) the cognitive limitations in decision making that often result in irrational but predictable decision‐making behavior as demonstrated by empirical research on newsvendor behavior. This exercise can help instructors and students transition into broader discussions on human behavior, cognitive limitations, and perceptions of risk. Overall, it should provide the student with an improved understanding of the operational and behavioral issues associated with decision making under risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".