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When Suboptimal Rules
Open accessAbstract
This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.
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“recent work along these lines has found that algorithmic accuracy does not always lead to the optimal outcomes”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as Elmalech et al., 2015
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The Principles and Limits of Algorithm-in-the-Loop Decision Making
Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392
Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments
Green, B., & Chen, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287563 · Cited by 275
The flaws of policies requiring human oversight of government algorithms
Green, B. (2022) · Computer Law & Security Review · doi:10.1016/j.clsr.2022.105681 · Cited by 263
Metadata
- Authors
- Elmalech, A., Sarne, D., Rosenfeld, A., & Erez, E.
- Affiliations
- Bar-Ilan University (IL)
- Jerusalem College of Technology (IL)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2015-02-16
- Volume
- 29
- Issue
- 1
- Pages
- 1313-1319
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W303716618
- Cited by
- 15
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 4 · 2015: 1
- FWCI
- 1.50
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- National Conference on Artificial Intelligence
- Funders
- Israel Science Foundation (1083/13)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- human-agent interaction, strategy improvement
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: When Suboptimal Rules. https://app.cochat.ai/papers/10.1609/aaai.v29i1.9335
- The paper
- Elmalech, A., Sarne, D., Rosenfeld, A., & Erez, E. (2015). When Suboptimal Rules. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v29i1.9335
