Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion

Andreas Haupt

TL;DR

This paper studies how noisy measurement of consumer preferences in recommender systems affects market concentration and inequality. It casts the problem as Bayesian Persuasion, where a sender designs a measurement structure and posterior beliefs over two types to influence content allocation. The authors show that, under symmetric noise captured by a midplane involution, minority content is allocated no more than its population share, implying increased concentration and potential inequity, while general noise can yield broader outcomes up to twice the minority incidence. For Gaussian noise, minority allocation falls as the noise level increases, and the utilities of minority and majority agents obey convex-closure constraints, highlighting a trade-off between information accuracy and fairness calibration in personalization systems. These results offer a nuanced view of when measurement error harms or helps equity and concentration in two-type markets, with implications for calibration in algorithmic fairness.

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.

Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion

TL;DR

This paper studies how noisy measurement of consumer preferences in recommender systems affects market concentration and inequality. It casts the problem as Bayesian Persuasion, where a sender designs a measurement structure and posterior beliefs over two types to influence content allocation. The authors show that, under symmetric noise captured by a midplane involution, minority content is allocated no more than its population share, implying increased concentration and potential inequity, while general noise can yield broader outcomes up to twice the minority incidence. For Gaussian noise, minority allocation falls as the noise level increases, and the utilities of minority and majority agents obey convex-closure constraints, highlighting a trade-off between information accuracy and fairness calibration in personalization systems. These results offer a nuanced view of when measurement error harms or helps equity and concentration in two-type markets, with implications for calibration in algorithmic fairness.

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.
Paper Structure (5 sections, 6 theorems, 25 equations, 3 figures)

This paper contains 5 sections, 6 theorems, 25 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Model
  3. The Consequences of Measurement Error
  4. Market Concentration
  5. Consumer Welfare

Key Result

Proposition 1

For any $p \in [0, 2\alpha]$, there is a measurement structure $\sigma$ such that $\mathbb{P}[x_\textsf{min}] = p$. For any measurement structure, $\mathbb{P}[x_\textsf{min}] \le 2 \alpha$.

Figures (3)

  • Figure 1: The information structure maximizing minority content allocation.
  • Figure 2: The midplane reflection.
  • Figure 3: Achievable consumer utilities under measurement error.

Theorems & Definitions (13)

  • Proposition 1
  • proof
  • Definition 1
  • Theorem 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Theorem 2
  • ...and 3 more