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Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion

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  • Andreas Haupt

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.

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  • Andreas Haupt, 2025. "Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion," Papers 2510.16972, arXiv.org.
    • Handle: RePEc:arx:papers:2510.16972
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    1. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
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