@inproceedings{a53c0b07c2dc4f5b9f1a7e4502a525a8,
title = "Generative model of bid sequences in lowest unique bid auctions",
abstract = "Lowest unique bid auction (LUBA) sites are gaining popularity on the Internet in recent years. In this paper, we study LUBA with resubmission in discrete bid spaces. A long-standing goal in the field of Internet auction is to develop agents that can perceive and understand the strategy information behind the mechanism and can guide us to behave in a fast, frugal and smart way. We marry ideas from recurrent neural network and data to learn a generative model for generating winning bid sequences. A sequence of winning bids in Internet auctions can be viewed as a sequence of events and modeled by generative models. We learn a model that is able to capture the long dependencies in a winning bid sequence. The generated data obtained from our model and the ground truth dataset share similar distributions.",
keywords = "LUBA, auction, game theory, generative model, recurrent neural network",
author = "Yida Xu and Hamidou Tembine",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 30th Chinese Control and Decision Conference, CCDC 2018 ; Conference date: 09-06-2018 Through 11-06-2018",
year = "2018",
month = jul,
day = "6",
doi = "10.1109/CCDC.2018.8407671",
language = "English (US)",
series = "Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3179--3184",
booktitle = "Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018",
}