Violations of Monotonicity in Judgment and Decision Making
Abstract
Monotonicity is a fundamental assumption of axiomatic decision theories. According to this principle, if two alternatives are otherwise identical, except one has an outcome for at least one nonempty state of nature that is preferred to the corresponding outcome for the other alternative, then the alternative with the better outcome should be preferred. Applied to judgments of the value of gambles, the principle states that the judged value of a gamble should increase monotonically as a function of each outcome, holding everything else constant. As appealing as this axiom is for normative theory, it has been systematically violated in experiments in which subjects judge cash values of gambles. The violation has not been observed in transparent, direct comparisons, but it has been replicated when the gambles are compared to a fixed set of cash values. The violations can be explained by the assumption that decision weights in judgment differ depending on the rank and also on the augmented sign (which is negative, zero, or positive). Violations of branch independence can also be explained by rank-dependent configural weighting. The pattern observed rules out the theory that subjects cancel common outcomes in comparison. The pattern is also opposite that predicted by the inverse-S weighting function used in cumulative prospect theory. Testable properties are suggested to distinguish different models of configural weighting.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Monotonicity rules are widely adopted because humans expect decisions to be affected by feature changes in a consistent direction.
“Such rules are widely adopted, as humans expect decisions to be affected by feature changes in a consistent direction (Birnbaum 2019;Selbst and Barocas 2018).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Birnbaum 2019
Cited by
Sorted by citations
A dual system model of preferences under risk
Mukherjee, K. (2010) · Psychological Review · doi:10.1037/a0017884 · Cited by 198 · see citing works
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
- Authors
- Birnbaum, M. H.
- Affiliations
- California State University, Fullerton (US)
- Corresponding author
- Birnbaum, Michael H.
- ORCID
- Birnbaum, Michael H.
- Venue
- Choice, Decision, and Measurement: Essays in Honor of R. Duncan Luce
- Publisher
- Routledge
- Published
- 2019-05-23
- Pages
- 73-100
- OpenAlex
- W2962604095
- Cited by
- 85
- References
- 0
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 4 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 6.68
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Keywords
- monotonicity, decision weights, cumulative prospect theory, axiomatic decision theory
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