Artificial Intelligence, Algorithmic Recommendations and Competition
SSRN Electronic Journal
2023
- 6Citations
- 3,898Usage
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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
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