Eliciting Informed Preferences
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Abstract (arXiv)
In many settings -- like market research and social choice -- people may be presented with unfamiliar options. Classical mechanisms may perform poorly because they fail to incentivize people to learn about these options, or worse, encourage counterproductive information acquisition. We formalize this problem in a model of robust mechanism design where agents find it costly to learn about their values for a product or policy. We identify sharp limits on the designer's ability to elicit, or learn about, these values. Where these limits do not bind, we propose two-stage mechanisms that are detail-free and robust: the second stage is a classical mechanism and the first stage asks participants to predict the results of the second stage.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2025.
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1 paper.
- 2025Misaligned by Design: Incentive Failures in Machine Learning10 Nov 2025