Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:5052-5086, 2023.

Abstract

In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal social welfare and discuss bidders’ incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-chen23ac, title = {Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets}, author = {Chen, Yurong and Wang, Qian and Duan, Zhijian and Sun, Haoran and Chen, Zhaohua and Yan, Xiang and Deng, Xiaotie}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {5052--5086}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/chen23ac/chen23ac.pdf}, url = {https://proceedings.mlr.press/v202/chen23ac.html}, abstract = {In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal social welfare and discuss bidders’ incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.} }
Endnote
%0 Conference Paper %T Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets %A Yurong Chen %A Qian Wang %A Zhijian Duan %A Haoran Sun %A Zhaohua Chen %A Xiang Yan %A Xiaotie Deng %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-chen23ac %I PMLR %P 5052--5086 %U https://proceedings.mlr.press/v202/chen23ac.html %V 202 %X In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal social welfare and discuss bidders’ incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
APA
Chen, Y., Wang, Q., Duan, Z., Sun, H., Chen, Z., Yan, X. & Deng, X.. (2023). Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:5052-5086 Available from https://proceedings.mlr.press/v202/chen23ac.html.

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