Optimal Dynamic Auctions Are Virtual Welfare Maximizers

Authors: Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, Song Zuo2125-2132

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we show that any optimal dynamic auction is a virtual welfare maximizer subject to some monotone allocation constraints. We further develop an ironing technique that gets rid of the monotone allocation constraints. Quite different from Myerson s ironing approach, our technique is more technically involved due to the interdependence of the virtual value functions across buyers. We nevertheless show that ironing can be done approximately and efficiently, which in turn leads to a Fully Polynomial Time Approximation Scheme of the optimal dynamic auction.
Researcher Affiliation Collaboration Vahab Mirrokni Google Research mirrokni@google.com Renato Paes Leme Google Research renatoppl@google.com Pingzhong Tang Tsinghua University kenshinping@gmail.com Song Zuo Google Research szuo@google.com
Pseudocode No The paper discusses algorithmic approaches but does not present a structured pseudocode or algorithm block.
Open Source Code No The paper does not provide concrete access to source code for the described methodology. A link to a full version of the paper is provided, but this is not for code.
Open Datasets No The paper is theoretical and does not describe experiments using publicly available datasets. Therefore, no information about public datasets is provided.
Dataset Splits No The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing.
Hardware Specification No The paper focuses on theoretical aspects and does not provide details about hardware specifications used for experiments.
Software Dependencies No The paper is theoretical and does not specify software dependencies with version numbers for reproducibility.
Experiment Setup No The paper is theoretical and does not describe specific experimental setups or hyperparameter values.