Information Elicitation from Teams of Privacy-Conscious Experts

32 Pages Posted: 20 Oct 2022 Last revised: 9 Sep 2023

See all articles by Ruslan Momot

Ruslan Momot

University of Michigan, Stephen M. Ross School of Business

Marat Salikhov

New Economic School; SKOLKOVO Moscow School of Management

Date Written: October 14, 2022


Companies often inform their decisions by eliciting votes from teams of internal experts – for example, their employees. However, such experts may vote against their true beliefs if pressured by powerful stakeholders, whose interests may be misaligned with the company’s best interest. The experts' desire to protect their true beliefs from such stakeholders presents a privacy concern and makes eliciting their information a challenge. This paper explores how such privacy concerns can be addressed by designing appropriate vote elicitation processes. We build a parsimonious economic model of a firm making a binary decision based on binary votes from a team of experts. The latter observe private noisy signals about an unknown state of the world and decide whether to vote truthfully based on their signals. The experts weigh (i) the benefit of making the firm’s decision more accurate by voting truthfully against (ii) the cost of losing privacy by exposing their true beliefs. We show that without intervention from the firm’s side, privacy concerns undermine the conventional “wisdom of crowds” logic – in which having a larger pool of experts strictly improves the quality of the firm’s decisions. Instead, when experts are privacy-conscious, the firm prefers a finite team size, since larger teams unravel due to free-riding by the experts. Because of this issue, the firm cannot always use the insights of all available experts. To alleviate this problem, we propose combining two mechanisms – adding noise to experts’ votes and paying bonuses for vote accuracy. Our analysis shows that using these mechanisms may restore truthful voting and ensure that the firm can make use of all available experts. Our numerical extensions further support the practicality of our approach, suggesting that in certain settings, even complete anonymization – a seemingly simpler and more practical option – is outperformed by the mechanisms we propose.

Keywords: differential privacy, information elicitation, privacy preservation, voting, wisdom of crowds

JEL Classification: C72, D81, D82, M20, M10

Suggested Citation

Momot, Ruslan and Salikhov, Marat, Information Elicitation from Teams of Privacy-Conscious Experts (October 14, 2022). Available at SSRN: or

Ruslan Momot (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States


Marat Salikhov

New Economic School ( email )

100A Novaya Street
Moscow, Skolkovo 143026


SKOLKOVO Moscow School of Management ( email )

1st km of Skolkovo highway
Odintsovsky District
Moscow 115035

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