Policy Design for Two-sided Platforms with Participation Dynamics

Haruka Kiyohara, Fan Yao, Sarah Dean
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:30966-30985, 2025.

Abstract

In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while providers benefit from increases in viewer population. 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 recommender 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 the standard “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 social welfare by taking the population effects into account, and demonstrate its effectiveness in synthetic and real-data experiments. Our experiment code is available at https://github.com/sdean-group/dynamics-two-sided-market.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-kiyohara25a, title = {Policy Design for Two-sided Platforms with Participation Dynamics}, author = {Kiyohara, Haruka and Yao, Fan and Dean, Sarah}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {30966--30985}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/kiyohara25a/kiyohara25a.pdf}, url = {https://proceedings.mlr.press/v267/kiyohara25a.html}, abstract = {In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while providers benefit from increases in viewer population. 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 recommender 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 the standard “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 social welfare by taking the population effects into account, and demonstrate its effectiveness in synthetic and real-data experiments. Our experiment code is available at https://github.com/sdean-group/dynamics-two-sided-market.} }
Endnote
%0 Conference Paper %T Policy Design for Two-sided Platforms with Participation Dynamics %A Haruka Kiyohara %A Fan Yao %A Sarah Dean %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-kiyohara25a %I PMLR %P 30966--30985 %U https://proceedings.mlr.press/v267/kiyohara25a.html %V 267 %X In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while providers benefit from increases in viewer population. 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 recommender 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 the standard “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 social welfare by taking the population effects into account, and demonstrate its effectiveness in synthetic and real-data experiments. Our experiment code is available at https://github.com/sdean-group/dynamics-two-sided-market.
APA
Kiyohara, H., Yao, F. & Dean, S.. (2025). Policy Design for Two-sided Platforms with Participation Dynamics. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:30966-30985 Available from https://proceedings.mlr.press/v267/kiyohara25a.html.

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