Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion

Andreas Haupt

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Abstract: Algorithmic recommendation based on noisy preference measurement is prevalent in recommendation systems. This paper discusses the consequences of such recommendation on market concentration and inequality. Binary types denoting a statistical majority and minority are noisily revealed through a statistical experiment. The achievable utilities and recommendation shares for the two groups can be analyzed as a Bayesian Persuasion problem. While under arbitrary noise structures, effects on concentration compared to a full-information market are ambiguous, under symmetric noise, concentration increases and consumer welfare becomes more unequal. We define symmetric statistical experiments and analyze persuasion under a restriction to such experiments, which may be of independent interest.

Date: 2025-10
New Economics Papers: this item is included in nep-com, nep-exp and nep-mic
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