TY - GEN
T1 - Selling to Multiple No-Regret Buyers
AU - Cai, Linda
AU - Weinberg, S. Matthew
AU - Wildenhain, Evan
AU - Zhang, Shirley
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - We consider the problem of repeatedly auctioning a single item to multiple i.i.d buyers who each use a no-regret learning algorithm to bid over time. In particular, we study the seller’s optimal revenue, if they know that the buyers are no-regret learners (but only that their behavior satisfies some no-regret property—they do not know the precise algorithm/heuristic used). Our main result designs an auction that extracts revenue equal to the full expected welfare whenever the buyers are “mean-based” (a property satisfied by standard no-regret learning algorithms such as Multiplicative Weights, Follow-the-Perturbed-Leader, etc.). This extends a main result of [4] which held only for a single buyer. Our other results consider the case when buyers are mean-based but never overbid. On this front, [4] provides a simple LP formulation for the revenue-maximizing auction for a single-buyer. We identify several formal barriers to extending this approach to multiple buyers.
AB - We consider the problem of repeatedly auctioning a single item to multiple i.i.d buyers who each use a no-regret learning algorithm to bid over time. In particular, we study the seller’s optimal revenue, if they know that the buyers are no-regret learners (but only that their behavior satisfies some no-regret property—they do not know the precise algorithm/heuristic used). Our main result designs an auction that extracts revenue equal to the full expected welfare whenever the buyers are “mean-based” (a property satisfied by standard no-regret learning algorithms such as Multiplicative Weights, Follow-the-Perturbed-Leader, etc.). This extends a main result of [4] which held only for a single buyer. Our other results consider the case when buyers are mean-based but never overbid. On this front, [4] provides a simple LP formulation for the revenue-maximizing auction for a single-buyer. We identify several formal barriers to extending this approach to multiple buyers.
UR - https://www.scopus.com/pages/publications/85181978758
UR - https://www.scopus.com/pages/publications/85181978758#tab=citedBy
U2 - 10.1007/978-3-031-48974-7_7
DO - 10.1007/978-3-031-48974-7_7
M3 - Conference contribution
AN - SCOPUS:85181978758
SN - 9783031489730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 129
BT - Web and Internet Economics - 19th International Conference, WINE 2023, Proceedings
A2 - Garg, Jugal
A2 - Klimm, Max
A2 - Kong, Yuqing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th InternationalConference on Web and Internet Economics, WINE 2023
Y2 - 4 December 2023 through 8 December 2023
ER -