Privacy, Voting, and the Wisdom of Crowds

When Privacy Concerns Undermine the Wisdom of Crowds Organizations often rely on committees, internal experts, and employee reports to make high-stakes decisions. But experts may withhold or distort private information when truthful reporting could expose them to retaliation or other personal costs. In “Privacy, Voting, and the Wisdom of Crowds,” accepted at Operations Research, Ekaterina Astashkina, Ruslan Momot, and Marat Salikhov study how privacy concerns affect collective decision making. The paper shows that privacy concerns can break the usual wisdom-of-crowds logic: adding more experts may eventually reduce, rather than improve, information aggregation because each expert becomes less pivotal and more tempted to hide their signal. The authors characterize mechanisms that combine randomized response—garbling votes to provide plausible deniability—with accuracy-based rewards. These tools can restore truthful voting and, under appropriate conditions, allow organizations to recover near-perfect learning from expert groups.

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Publication Details

Journal
Operations Research
Published
2026-05-29
DOI
https://doi.org/10.1287/opre.2023.0671
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
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article

Privacy, Voting, and the Wisdom of Crowds

Ruslan Momot, Marat Salikhov, Ekaterina Astashkina
Operations Research
Mobile Crowdsensing and Crowdsourcing
article

Privacy, Voting, and the Wisdom of Crowds

Ruslan Momot, Marat Salikhov, Ekaterina Astashkina
article en

Abstract

When Privacy Concerns Undermine the Wisdom of Crowds Organizations often rely on committees, internal experts, and employee reports to make high-stakes decisions. But experts may withhold or distort private information when truthful reporting could expose them to retaliation or other personal costs. In “Privacy, Voting, and the Wisdom of Crowds,” accepted at Operations Research, Ekaterina Astashkina, Ruslan Momot, and Marat Salikhov study how privacy concerns affect collective decision making. The paper shows that privacy concerns can break the usual wisdom-of-crowds logic: adding more experts may eventually reduce, rather than improve, information aggregation because each expert becomes less pivotal and more tempted to hide their signal. The authors characterize mechanisms that combine randomized response—garbling votes to provide plausible deniability—with accuracy-based rewards. These tools can restore truthful voting and, under appropriate conditions, allow organizations to recover near-perfect learning from expert groups.

Operations Research
University of Michigan (US), Skolkovo Moscow School of Management (RU), Ross School (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 3%
Mobile Crowdsensing and Crowdsourcing
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