Policy Design for Two-sided Platforms with Participation Dynamics

• DOI: 10.48550/arxiv.2502.01792
• Publication Date: 2025-01-01
• Authors: Kiyohara, Haruka; Yao, Fan; Dean, Sarah
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computer Science and Game Theory; FOS: Electrical engineering, electronic engineering, information engineering; Systems and Control (eess.SY); Electrical Engineering and Systems Science - Systems and Control; Information Retrieval (cs.IR); Computer Science - Information Retrieval; Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)

preprint, under review<br/>In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics, where an increased provider population results in higher viewer utility and the increase of viewer population results in higher provider utility. Despite the importance of such "population effects" on long-term platform health, recommendation policies do not generally take the participation dynamics into account. This paper thus studies the dynamics and policy design on two-sided platforms under the population effects for the first time. Our control- and game-theoretic findings warn against the use of myopic-greedy policy and shed light on the importance of provider-side considerations (i.e., effectively distributing exposure among provider groups) to improve social welfare via population growth. We also present a simple algorithm to optimize long-term objectives by considering the population effects, and demonstrate its effectiveness in synthetic and real-data experiments.<br/>