Over and under commitment to a course of action in decisions from experience.
Open accessAbstract
Many natural activities involve "stopping dilemmas": situations that require a repeated decision between investing effort to achieve some valued goal and stopping that effort to try something else. Previous research into these problems highlights two contradicting biases. While one class of studies suggests a tendency to stop too late (e.g., escalation of commitment), another class of studies suggests a tendency to give up too early (e.g., learned helplessness). Our paper clarifies the conditions that trigger these biases by focusing on two factors: the decision mode (ongoing decisions vs. planning in advance) and the probability each search effort will be costly. We find that experience with stopping dilemmas produces a reversed sunk-cost effect: Most participants stop too early when search is frequently costly but stop too late when search is usually rewarding. This effect can be explained by assuming that stopping decisions reflect reliance on small samples of past experiences with similar stopping dilemmas. Comparison of ongoing and planning decisions reveals an interaction: planning in advance increased search when searching was frequently costly, but decreased search when most search efforts were rewarding. This interaction can be explained by assuming a contingent re-evaluation process: Recent losses increase the tendency to reevaluate a plan to continue the search, and recent gains increase the tendency to reevaluate a plan to stop. In addition, we observe a preferenc…
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People tend to abandon optimal strategies when they involve frequent friction and to overexploit suboptimal strategies that are frequently rewarding, but ultimately very costly.
“People tend to abandon optimal strategies when they involve frequent friction and to overexploit suboptimal strategies that are frequently rewarding, but ultimately very costly (Cohen & Erev, 2021).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Cohen & Erev, 2021
Cites
Knee-deep in the big muddy: a study of escalating commitment to a chosen course of action
Staw, B. M. (1976) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(76)90005-2 · Cited by 2,061 · cited 3× · see citing works
“For example, it is possible that this conflict partly contributed to the escalation of US involvement in the Vietnam war (Staw, 1976).”
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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 2 · see citing works
Self-citation
- Authors
- Cohen, D., & Erev, I.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Venue
- Journal of Experimental Psychology: General
- Published
- 2021-12
- Volume
- 150
- Issue
- 12
- Pages
- 2455-2471
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- OpenAlex
- W3198640686
- Cited by
- 9
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2020: 1
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- green
- edoc.unibas.ch
- Licence
- other-oa
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion, other-oa)
- edoc (University of Basel)
- Funders
- I-CORE Program of the Planning and Budgeting Committee
- Israel Science Foundation (535/17)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- escalation of commitment, learned helplessness, sunk cost effect, search effort, search costs, exploration, problem space, decision bias
- MeSH terms
- Decision Making, Humans, Probability, Reward, Bias
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