Eliciting Informed Preferences

TL;DR

This paper explores how to design mechanisms that effectively elicit informed preferences when agents face costly learning, proposing robust, detail-free two-stage mechanisms that combine prediction and classical methods.

Contribution

It formalizes the problem of preference elicitation with costly learning and introduces a novel two-stage mechanism approach that is robust and detail-free.

Findings

Sharp limits on the ability to elicit true preferences under costly learning.

Proposed two-stage mechanisms that improve preference elicitation.

Mechanisms are robust and do not require detailed prior information.

Abstract

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.