SEMINARS

Webinar by Emilio Calvano, Un. of Bologna: Artificial Intelligence, Algorithmic Recommendations and Competition"

SPEAKER

Emilio Calvano,

University of Bologna










DATE

Apr. 30th, 2024

11:00 to 12:00 London time.


LOCATION

Event will be held online

TECHNIS is pleased to invite you to a free webinar. TECHNIS webinars focus on IP and innovation examining recent legal, economic, managerial, ethical and policy issues related to technological innovation. Our approach is interdisciplinary and presentations are given by experts in different fields such as economics, law, management, STS, sociology, anthropology and philosophy. Webinar presentations last for 20min and are followed by a 40min discussion.


Please join us for a webinar on Tuesday the 30th of April 2024 at 11:00 London time i.e. 12:00 Brussels time, 13:00 Athens time. The speaker is Emilio Calvano, University of Bologna, University of ToulouseThe title of the talk isArtificial Intelligence, Algorithmic Recommendations and Competition".


This webinar is free and open to all. The moderator is Dr. Andreas Panagopoulos


Join Zoom Meeting

https://uoc-gr.zoom.us/j/82503357021?pwd=Tms4aGQ1UlNKRmFxWWh3YlN4N3N1QT09

Meeting ID: 825 0335 7021

Passcode: 844249


NOTE: To participate please contact Andreas Panagopoulos at least an hour prior to the webinar.


Abstract: 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.