The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence
Abstract
In the newsvendor problem a decision maker orders inventory before a one period selling season with stochastic demand. If too much is ordered, stock is left over at the end of the period, whereas if too little is ordered, sales are lost. The expected profit-maximizing order quantity is well known, but little is known about how managers actually make these decisions. We describe two experiments that investigate newsvendor decisions across different profit conditions. Results from these studies demonstrate that choices systematically deviate from those that maximize expected profit. Subjects order too few of high-profit products and too many of low-profit products. These results are not consistent with risk-aversion, risk-seeking preferences, Prospect Theory preferences, waste aversion, stockout aversion, or the consequences of underestimating opportunity costs. Two explanations are consistent with the data. One, subjects behave as if their utility function incorporates a preference to reduce ex-post inventory error, the absolute difference between the chosen quantity and realized demand. Two, subjects suffer from the anchoring and insufficient adjustment bias. Feedback and training did not mitigate inventory order errors. We suggest techniques to improve decision making.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D., & Tversky, A. (1988) · Decision, Probability and Utility · doi:10.1017/cbo9780511609220.014 · Cited by 32,474
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Judgment under Uncertainty: Heuristics and Biases
(1982) · doi:10.1017/cbo9780511809477 · Cited by 8,240
Judgment Under Uncertainty: Heuristics and Biases.
Shafer, G. R., Kahnerman, D., Slovic, P., & Tversky, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288362 · Cited by 5,657
Judgment under uncertainty: List of contributors
Kahneman, D. J., Slovic, P., & Tversky, A. (1982) · Cited by 4,670
Judgment under uncertainty: Causality and attribution
Kahneman, D. J., Slovic, P., & Tversky, A. (1982) · Cited by 4,670
Regret in Decision Making under Uncertainty
Bell, D. E. (1982) · Operations Research · doi:10.1287/opre.30.5.961 · Cited by 2,977
Reengineering Work: Don’t Automate, Obliterate
Hammer, M. M. (1990) · Cited by 2,908
Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment
Sterman, J. D. (1989) · Management Science · doi:10.1287/mnsc.35.3.321 · Cited by 2,500
Cited by
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230
“…of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes, Faust, & Meehl, 1989; Grove et al., 2000), demand for products (Schweitzer & Cachon, 2000), and so on (for reviews see Dawes et al., 1989; Grove et al., 2000; Meehl, 1954).”
BackgroundIs timely information always better? The effect of feedback frequency on decision making
Lurie, N. H., & Swaminathan, J. M. (2009) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2008.05.005 · Cited by 264
Metadata
- Authors
- Schweitzer, M. E., & Cachon, G. P.
- Venue
- Management Science
- Published
- 2000-03
- Volume
- 46
- Issue
- 3
- Pages
- 404-420
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2130960228
- Cited by
- 1,083
- References
- 19
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence. https://app.cochat.ai/papers/10.1287/mnsc.46.3.404.12070
- The paper
- Schweitzer, M. E., & Cachon, G. P. (2000). Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence. Management Science. https://doi.org/10.1287/mnsc.46.3.404.12070
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.