Frontiers: Digital Hermits

doi:10.1287/mksc.2023.0612

Frontiers: Digital Hermits

2024-07-01

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AI

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Jeanine Miklós‐Thal *

DOI：10.1287/mksc.2023.0612

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Abstract

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When users share multidimensional data about themselves with a firm, the firm learns about the correlations between different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. Each user can share no data, only nonsensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user's privacy-sensitive data from their nonsensitive data, the share of new users who share no data (digital hermits) grows. This growth of digital hermits occurs even though the firm offers higher compensation for a user's nonsensitive data and a user's full data as its ability to draw inferences improves. At the same time, the share of new users who share their full data also grows. The model thus predicts a polarization of users' datasharing choices away from nonsensitive data sharing to no sharing and full sharing. Our model suggests that recent privacy policies, which are focused on control of data rather than inferences, may be misplaced.

Keywords:

privacy

data

digital markets

prediction

game theory

microeconomics

Frontiers: Digital Hermits

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