More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
Abstract
Query learning models from computational learning theory (CLT) can be adopted to perform elicitation in combinatorial auctions. Indeed, a recent elicitation framework demonstrated that the equivalence queries of CLT can be usefully simulated with price-based demand queries. In this paper, we validate the flexibility of this framework by defining a learning algorithm for atomic bidding languages, a class that includes XOR and OR. We also handle incentives, characterizing the communication requirements of the Vickrey-Clarke-Groves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium. 1
Cite
Text
Lahaie et al. "More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Lahaie et al. "More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/lahaie2005ijcai-more/)BibTeX
@inproceedings{lahaie2005ijcai-more,
title = {{More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives}},
author = {Lahaie, Sébastien and Constantin, Florin and Parkes, David C.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {959-964},
url = {https://mlanthology.org/ijcai/2005/lahaie2005ijcai-more/}
}