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SSRN
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Artificial Intelligence, Algorithmic Recommendations and Competition

SSRN Electronic Journal
2023
  • 6
    Citations
  • 3,898
    Usage
  • 7
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    6
    • Policy Citations
      4
      • Policy Citation
        4
    • Citation Indexes
      2
  • Usage
    3,898
    • Abstract Views
      2,689
    • Downloads
      1,209
  • Captures
    7
  • Ratings
    • Download Rank
      35,485

Article Description

We present a methodology for analyzing the impact of algorithmic recommendations on product market competition, addressing concerns that have been raised in both academic and policy circles regarding their potential anti-competitive effects. Our analysis demonstrates that recommender systems (RSs) lead to higher market concentration and prices compared to a scenario where algorithmic recommendations are unavailable and consumers rely solely on individual search. However, RSs also improve the match between products and consumers and reduce the need for expensive search processes. By accounting for both the positive and negative effects, we find that RSs are likely to increase consumer surplus for reasonable parameter values. However, increasing the amount of data available to the algorithms may lead to a reduction in consumer surplus. We also examine the potential for manipulation of recommendations and its impact on competition, finding that such manipulation is more likely to represent an exclusionary abuse than an exploitative one.

Bibliographic Details

Emilio Calvano; Giacomo Calzolari; Vincenzo Denicolò; Sergio Pastorello

Elsevier BV

Artificial Intelligence; Recommender Systems; Search; Product Market Competition; Manipulation; Biased Recommendations; Niche Products.

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