Algorithmic Collusion or Competition: the Role of Platforms' Recommender
Systems
- URL: http://arxiv.org/abs/2309.14548v1
- Date: Mon, 25 Sep 2023 21:45:30 GMT
- Title: Algorithmic Collusion or Competition: the Role of Platforms' Recommender
Systems
- Authors: Xingchen Xu, Stephanie Lee, Yong Tan
- Abstract summary: This study examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms.
Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives.
A demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals.
- Score: 2.551933457838182
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent academic research has extensively examined algorithmic collusion
resulting from the utilization of artificial intelligence (AI)-based dynamic
pricing algorithms. Nevertheless, e-commerce platforms employ recommendation
algorithms to allocate exposure to various products, and this important aspect
has been largely overlooked in previous studies on algorithmic collusion. Our
study bridges this important gap in the literature and examines how
recommendation algorithms can determine the competitive or collusive dynamics
of AI-based pricing algorithms. Specifically, two commonly deployed
recommendation algorithms are examined: (i) a recommender system that aims to
maximize the sellers' total profit (profit-based recommender system) and (ii) a
recommender system that aims to maximize the demand for products sold on the
platform (demand-based recommender system). We construct a repeated game
framework that incorporates both pricing algorithms adopted by sellers and the
platform's recommender system. Subsequently, we conduct experiments to observe
price dynamics and ascertain the final equilibrium. Experimental results reveal
that a profit-based recommender system intensifies algorithmic collusion among
sellers due to its congruence with sellers' profit-maximizing objectives.
Conversely, a demand-based recommender system fosters price competition among
sellers and results in a lower price, owing to its misalignment with sellers'
goals. Extended analyses suggest the robustness of our findings in various
market scenarios. Overall, we highlight the importance of platforms'
recommender systems in delineating the competitive structure of the digital
marketplace, providing important insights for market participants and
corresponding policymakers.
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