Preference elicitation in combinatorial auctions

In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). We build on prior work where preference elicitation was done via kernelized support vector regressions (SVRs). However, the SVR-based approach has limitations because it requires solving a machine learning (ML)-based winner determination problem (WDP). With expressive kernels (like gaussians), the ML-based WDP cannot be solved for large domains. While linear or quadratic kernels have better computational scalability, these kernels have limited expressiveness. In this work, we address these shortcomings by using deep neural networks (DNNs) instead of SVRs. We first show how the DNN-based WDP can be reformulated into a mixed integer program (MIP). Second, we experimentally compare the prediction performance of DNNs against SVRs. Third, we present experimental evaluations in two medium-sized domains which show that even ICAs based on relatively small-sized DNNs lead to higher economic efficiency than ICAs based on kernelized SVRs. Finally, we show that our DNN-powered ICA also scales well to very large CA domains.

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Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions

Learning Theory - Lecture Notes in Computer Science ◽

10.1007/978-3-540-27819-1_1 ◽

2004 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Paolo Santi ◽

Vincent Conitzer ◽

Tuomas Sandholm

Keyword(s):

Preference Elicitation ◽

Combinatorial Auctions

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Preference elicitation in combinatorial auctions

Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 2 - AAMAS '02 ◽

10.1145/544741.544781 ◽

2002 ◽

Cited By ~ 1

Author(s):

Wolfram Conen ◽

Tuomas Sandholm

Keyword(s):

Preference Elicitation ◽

Combinatorial Auctions

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Combinatorial Auctions via Machine Learning-based Preference Elicitation

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/18 ◽

2018 ◽

Cited By ~ 1

Author(s):

Gianluca Brero ◽

Benjamin Lubin ◽

Sven Seuken

Keyword(s):

Machine Learning ◽

Value Function ◽

Allocative Efficiency ◽

Preference Elicitation ◽

Combinatorial Auctions ◽

Prior Work ◽

Spectrum Auction

Combinatorial auctions (CAs) are used to allocate multiple items among bidders with complex valuations. Since the value space grows exponentially in the number of items, it is impossible for bidders to report their full value function even in medium-sized settings. Prior work has shown that current designs often fail to elicit the most relevant values of the bidders, thus leading to inefficiencies. We address this problem by introducing a machine learning-based elicitation algorithm to identify which values to query from the bidders. Based on this elicitation paradigm we design a new CA mechanism we call PVM, where payments are determined so that bidders’ incentives are aligned with allocative efficiency. We validate PVM experimentally in several spectrum auction domains, and we show that it achieves high allocative efficiency even when only few values are elicited from the bidders.

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