The DOI matches its Crossref record.
Human judgment and social policy: irreducible uncertainty, inevitable error, unavoidable injustice
Abstract
Preface PART I: RIVALRY 1. Irreducible Uncertainty and the Need for Judgment 2. Duality of Error and Policy Formation 3. Coping with Uncertainty: The Rivalry Between Intuition and Analysis PART II: TENSION 4. Origins of Tension Between Coherence and Correspondence Theories of Competence in Judgment and Decision Making 5. The Evolutionary Roots of Correspondence Competence PART III: COMPROMISE 6. Reducing Rivalry Through Compromise 7. Task Structure, Cognitive Change, and Pattern Recognition 8. Reducing Tension Between Coherence and Correspondence Through Constructive Complementarity PART IV: POSSIBILITIES 9. Is it possible to Learn by Intervening?
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
Cited by
Representativeness revisited: Attribute substitution in intuitive judgment.
Kahneman, D., & Frederick, S. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.004 · Cited by 3,476
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.025 · Cited by 2,349
Psychological testing and psychological assessment: A review of evidence and issues.
Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., Eisman, E. J., Kubiszyn, T. W., & Reed, G. M. (2001) · American Psychologist · doi:10.1037//0003-066x.56.2.128 · Cited by 1,469
Models of ecological rationality: The recognition heuristic.
Goldstein, D. G., & Gigerenzer, G. (2002) · Psychological Review · doi:10.1037/0033-295x.109.1.75 · Cited by 1,352
A Universal Model of Diagnostic Reasoning
Croskerry, P. (2009) · Academic Medicine · doi:10.1097/acm.0b013e3181ace703 · Cited by 1,016
Ægisdóttir, S., White, M. J., Spengler, P. M., Maugherman, A. S., Anderson, L. A., Cook, R. S., Nichols, C. N., Lampropoulos, G. K., Walker, B. S., Cohen, G., et al. (2006) · The Counseling Psychologist · doi:10.1177/0011000005285875 · Cited by 737
Unconscious influences on decision making: A critical review
Newell, B. R., & Shanks, D. R. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12003214 · Cited by 652
Clinical cognition and diagnostic error: applications of a dual process model of reasoning
Croskerry, P. (2009) · Advances in Health Sciences Education · doi:10.1007/s10459-009-9182-2 · Cited by 494
Editors' Comments: Styles of Theorizing and the Social Organization of Knowledge
(2013) · Academy of Management Review · doi:10.5465/amr.2013.0085 · Cited by 271
Correlations of cognitive reflection with judgments and choices
Campitelli, G., & Labollita, M. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500001066 · Cited by 166
Mental Contamination and the Debiasing Problem
Wilson, T. D., Centerbar, D. B., & Brekke, N. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.012 · Cited by 160
The calibration of expert judgment: heuristics and biases beyond the laboratory
Koehler, D. J., Brenner, L., & Griffin, D. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.041 · Cited by 146
Saxena, D., Badillo-Urquiola, K., Wisniewski, P. J., & Guha, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476089 · Cited by 134
“Prior research has also established that an irreducible degree of uncertainty exists with respect to the outcomes in the public sector [21, 34, 57] with both humans and algorithms likely to make mistakes.”
BackgroundTeachers’ decision-making: Data based or intuition driven?
Vanlommel, K., Van Gasse, R., Vanhoof, J., & Van Petegem, P. (2017) · International Journal of Educational Research · doi:10.1016/j.ijer.2017.02.013 · Cited by 123
Re-visioning risk assessment for human service decision making
Schwalbe, C. (2004) · Children and Youth Services Review · doi:10.1016/j.childyouth.2004.02.011 · Cited by 94
Kaufmann, E. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100028 · Cited by 36
Assessment of expert performance compared across professional domains.
Thomas, R. P., & Lawrence, A. (2018) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2018.03.009 · Cited by 23
How Do We Assess the Trustworthiness of AI? Introducing the Trustworthiness Assessment Model (TrAM)
Schlicker, N., Baum, K., Uhde, A., Sterz, S., Hirsch, M. C., & Langer, M. (2022) · doi:10.31234/osf.io/qhwvx · Cited by 79
How to Fool Yourself With Experiments in Testing Theories in Psychological Research.
Wittmann, W. W., & Klumb, P. L. (2006) · Strengthening research methodology: Psychological measurement and evaluation. · doi:10.1037/11384-010 · Cited by 6
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 1997-04-01
- Volume
- 34
- Issue
- 08
- Pages
- 34-4545-34-4545
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W1662711610
- Cited by
- 719
- References
- 0
- Type
- book review
- 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
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.
