Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 724

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation.

Open access
Weber, Elke U., Shafir, Sharoni, Blais, Ann-Renée·2004·Psychological Review

Abstract

This article examines the statistical determinants of risk preference. In a meta-analysis of animal risk preference (foraging birds and insects), the coefficient of variation (CV), a measure of risk per unit of return, predicts choices far better than outcome variance, the risk measure of normative models. In a meta-analysis of human risk preference, the superiority of the CV over variance in predicting risk taking is not as strong. Two experiments show that people's risk sensitivity becomes strongly proportional to the CV when they learn about choice alternatives like other animals, by experiential sampling over time. Experience-based choices differ from choices when outcomes and probabilities are numerically described. Zipf's law as an ecological regularity and Weber's law as a psychological regularity may give rise to the CV as a measure of risk.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1037/0033-295x.111.2.430

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 5 references

Cited by

See all 724

Metadata

Authors
Weber, E. U., Shafir, S., & Blais, A.-R.
Venue
Psychological Review
Published
2004
Volume
111
Issue
2
Pages
430-445
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
OpenAlex
W2132526093
Cited by
724
References
5
FWCI
12.63
Type
journal article
Language
en
Open access
green

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: Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation. https://app.cochat.ai/papers/10.1037/0033-295x.111.2.430
The paper
Weber, E. U., Shafir, S., & Blais, A.-R. (2004). Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation. Psychological Review. https://doi.org/10.1037/0033-295x.111.2.430

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.