2025/10/19 by Andreas Haupt, Haupt, Andreas
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Computers and Society (cs.CY) #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #FOS: Economics and business #Game Theory and Voting Systems #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2510.16972
openalex publication_date 2025/10/19 · openalex created_date 2025/10/22 · openalex updated_date 2026/07/28
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.