Department of Industrial Engineering & Decision Analytics [Joint IEDA/CSE] seminar - Tight Regret Bounds for Fixed-Price Bilateral Trade

We examine fixed-price mechanisms in bilateral trade through the lens of regret minimization. Our main results are twofold.

(i) For independent values, a near-optimal $\widetilde{\Theta}(T^{2/3})$ tight bound for {\sf Global Budget Balance} fixed-price mechanisms with two-bit/one-bit feedback.

(ii) For correlated/adversarial values, a near-optimal $\Omega(T^{3/4})$ lower bound for {\sf Global Budget Balance} fixed-price mechanisms with two-bit/one-bit feedback, which improves the best known $\Omega(T^{5/7})$ lower bound obtained in the work \cite{BCCF24} and, up to polylogarithmic factors, matches the $\widetilde{\mathcal{O}}(T^{3 / 4})$ upper bound obtained in the same work.

Our work in combination with the previous works \cite{CCCFL24mor, CCCFL24jmlr, AFF24, BCCF24} (essentially) gives a thorough understanding of regret minimization for fixed-price bilateral trade.

 

En route, we have developed two technical ingredients that might be of independent interest:

(i) A novel algorithmic paradigm, called {\em fractal elimination}, to address one-bit feedback and independent values.

(ii) A new {\em lower-bound construction} with novel proof techniques, to address the {\sf Global Budget Balance} constraint and correlated values.

Event Format
Speakers / Performers:
Dr. Yaonan JIN
Huawei TCS Lab

Yaonan Jin is a full-time researcher at the Huawei TCS Lab (lead by Pinyan Lu). His research interests encompass Theoretical Computer Science, with an emphasis on Algorithmic Economics. Before joining Huawei, he obtained his PhD from Columbia University in 2023 (advised by Xi Chen and Rocco Servedio). Before that, he obtained his MPhil from Hong Kong University of Science and Technology in 2019 (advised by Qi Qi) and his BEng from Shanghai Jiao Tong University in 2017.

Language
English
Recommended For
Faculty and staff
PG students
Organizer
Department of Industrial Engineering & Decision Analytics
Department of Computer Science and Engineering
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