NeurIPS 2015poster46 citations
Revenue Optimization against Strategic Buyers
Abstract
We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his $\gamma$-discounted surplus. To analyze this problem, we introduce the notion of epsilon-strategic buyer, a more natural notion of strategic behavior than what has been used in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in $O\Big( \log T + \frac{1}{\log(1/\gamma)} \Big)$ when the seller can offer prices from a finite set $\cP$ and provide a regret bound in $\widetilde O \Big(\sqrt{T} + \frac{T^{1/4}}{\log(1/\gamma)} \Big)$ when the buyer is offered prices from the interval $[0, 1]$.
BibTeX
@inproceedings{NIPS2015_55c567fd,
author = {Mohri, Mehryar and Munoz, Andres},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Revenue Optimization against Strategic Buyers},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/55c567fd4395ecef6d936cf77b8d5b2b-Paper.pdf},
volume = {28},
year = {2015}
}