The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
The impact of experience on the tendency to accept recommended defaults
Abstract
Two preregistered web studies are presented that explore the impact of experience on the tendency to accept recommended defaults. In each of the 100 trials, participants (n = 180, n = 165) could accept a recommended default option or choose a less attractive prospect. The location of the options (left or right) was randomly determined before each trial. Both studies compared two conditions. Under Condition Dominant, the default option maximized participants’ payoff in all trials. Under Condition Protective, the default option protected the participants from rare losses and maximized expected return but decreased payoff in most trials. The results reveal a tendency to accept the default in Condition Dominant but the opposite tendency in Condition Protective. This pattern was predicted by assuming that in addition to promoting specific actions, the presentation of the default changes the set of feasible strategies, and choice between these strategies reflects reliance on small samples of past experiences.
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“DfE research suggests that a history of small positive outcomes can increase the willingness to follow an expert's advice, even when it is suboptimal and carries large, rare losses (Roth et al., 2024).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Roth et al., 2024
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“Even when agents are nominally aligned, optimization for latency or engagement can favor low-friction interfaces that increase the likelihood users accept unsafe defaults, skip verification, and disclose sensitive information (Roth et al., 2024); (Cohen et al., 2025).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Roth et al., 2024
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Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
The Handbook of experimental economics
(1995) · Choice Reviews Online · doi:10.5860/choice.33-1645 · Cited by 3,679
oTree—An open-source platform for laboratory, online, and field experiments
Chen, D. L., Schonger, M., & Wickens, C. (2016) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2015.12.001 · Cited by 1,889
Recommendations Implicit in Policy Defaults
McKenzie, C. R. M., Liersch, M. J., & Finkelstein, S. R. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01721.x · Cited by 518
When and why defaults influence decisions: a meta-analysis of default effects
JACHIMOWICZ, J. M., DUNCAN, S., WEBER, E. U., & JOHNSON, E. J. (2019) · Behavioural Public Policy · doi:10.1017/bpp.2018.43 · Cited by 481
Partitioning default effects: Why people choose not to choose.
Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K. (2011) · Journal of Experimental Psychology: Applied · doi:10.1037/a0024354 · Cited by 340
A choice prediction competition: Choices from experience and from description
Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., Hertwig, R., Stewart, T., West, R., & Lebiere, C. (2010) · Journal of Behavioral Decision Making · doi:10.1002/bdm.683 · Cited by 329
Self-citationErev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017) · Psychological Review · doi:10.1037/rev0000062 · Cited by 197
Self-citationReliance on small samples, the wavy recency effect, and similarity-based learning.
Plonsky, O., Teodorescu, K., & Erev, I. (2015) · Psychological Review · doi:10.1037/a0039413 · Cited by 162
Self-citationNudge Your Customers Toward Better Choices
Goldstein, D. G., Johnson, E. J., Herrmann, A. P., & Heitmann, M. (2008) · Alexandria (UniSG) (University of St.Gallen) · Cited by 160
Research works printed but not linked: 1
- Plonsky, O. , Apel, R. , Ert, E. , Tennenholtz, M. , Bourgin, D. , Peterson, J. C. , & Erev, I . (2019). Predicting human decisions with behavioral theories and machine learning. preprint arXiv:1904.06866 · arXiv
Cited by
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 108
Metadata
- Authors
- Roth, Y., Waldman, G. M., & Erev, I.
- ORCID
- Venue
- Judgment and Decision Making
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024
- Volume
- 19
- ISSN
- 1930-2975
- Cited by
- 0
- References
- 27
- Language
- en
- Funders
- Israel Science Foundation (1857/22, 861/22)
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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).
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