The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Debiasing Decisions: Improved Decision Making With a Single Training Intervention
Abstract
From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, education, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ −31.94% and videos ≥ −18.60%) that persisted at least 2 months later (games ≥ −23.57% and videos ≥ −19.20%). Games that provided personalized feedback and practice produced larger effects than did videos. Debiasing effects were domain general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions.
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
(1994) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(94)92216-0 · Cited by 45,910
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
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
(2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6772-0 · Cited by 7,486
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267
Subjective probability: A judgment of representativeness
Kahneman, D., & Tversky, A. (1972) · Cognitive Psychology · doi:10.1016/0010-0285(72)90016-3 · Cited by 4,851
Moffitt, R. A. (2009) · American Economic Review · doi:10.1257/aer.99.2.660 · Cited by 4,694
(2004) · Choice Reviews Online · doi:10.5860/choice.41-3182 · Cited by 4,690
Human Inference: Strategies and Shortcomings of Social Judgment.
Cherniak, C., Nisbett, R., & Ross, L. (1983) · The Philosophical Review · doi:10.2307/2184495 · Cited by 4,196
Essentials of Behavioral Research: Methods and Data Analysis
Rosenthal, R., & Rosnow, R. L. (1984) · Cited by 3,925
Research works printed but not linked: 16
- Barton M., 2015, Digital Games Research Association Conference
- 10.2307/3312889 · DOI · Paper page
- 10.1177/237946151500100104 · doi:10.1177/237946151500100104
- Cooper J. R., 2005, Curing analytic pathologies: Pathways to improved intelligence analysis
- 10.1177/237946151500100102 · doi:10.1177/237946151500100102
- Haynes L., 2012, Test, learn, adapt: Developing public policy with randomized controlled trials
- Heuer R. J., 1999, Psychology of intelligence analysis
- Intelligence Advanced Research Projects Activity, 2012, Unbiasing your biases I
- Intelligence Advanced Research Projects Activity, 2013, Unbiasing your biases II
- 10.1175/1520-0477(1974)055<1206:SPFEIM>2.0.CO;2 · doi:10.1175/1520-0477(1974)055<1206:spfeim>2.0.co;2
- Silberman L. H., 2005, Report to the President of the United States
- 10.1037/0033-2909.119.1.3 · DOI · Paper page
- 10.1037/0022-3514.36.11.1202 · doi:10.1037/0022-3514.36.11.1202 · Paper page
- Symborski C., 2014, Interservice/Industry Training, Simulation and Education Conference
- Thaler R. H., 2008, Nudge
- 10.2307/1129583 · doi:10.2307/1129583 · Paper page
Cited by
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13232 · Cited by 589
Honig, S., & Oron-Gilad, T. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00861 · Cited by 338
“Three general approaches have been suggested and shown to produce positive results (Morewedge et al., 2015): (1) recalibrating incentives to reward healthy behavior, (2) optimizing how choice options are presented and obtained, and (3) debiasing training interventions.”
BackgroundThe mixed effects of online diversity training
Chang, E. H., Milkman, K. L., Gromet, D. M., Rebele, R. W., Massey, C., Duckworth, A. L., & Grant, A. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816076116 · Cited by 304
Parkhurst, J. (2016) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · doi:10.4324/9781315675008 · Cited by 756
Berthet, V. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.802439 · Cited by 191
Metadata
- Authors
- Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H., & Kassam, K. S.
- Venue
- Policy Insights from the Behavioral and Brain Sciences
- Publisher
- SAGE Publications
- Published
- 2015-08-13
- Volume
- 2
- Issue
- 1
- Pages
- 129-140
- ISSN
- 2372-7322 (ISSN-L)
- 2372-7330
- OpenAlex
- W2338305963
- Cited by
- 384
- References
- 96
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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: Debiasing Decisions: Improved Decision Making With a Single Training Intervention. https://app.cochat.ai/papers/10.1177/2372732215600886
- The paper
- Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H., & Kassam, K. S. (2015). Debiasing Decisions: Improved Decision Making With a Single Training Intervention. Policy Insights from the Behavioral and Brain Sciences. https://doi.org/10.1177/2372732215600886
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.