Fast Iterative Combinatorial Auctions via Bayesian Learning

Brero, Gianluca; Lahaie, Sébastien; Seuken, Sven

doi:10.1609/aaai.v33i01.33011820

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…This approach differs from ours, in that it uses ML to learn the whole mechanism. The work by Brero and Lahaie (2018) and Brero, Lahaie, and Seuken (2019) follows a design paradigm more similir to our ours (integrating ML into the auction), but it designs a Bayesian price-based mechanism, where the main goal is to improve the convergence speed of the auction.…”

Section: Related Workmentioning

confidence: 99%

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

Beyeler

Brero

Lubin

et al. 2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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We study the design of iterative combinatorial auctions for domains with a large number of items. In such domains, preference elicitation is a major challenge because the bundle space grows exponentially in the number of items. To keep preference elicitation manageable, recent work has employed a machine learning (ML) algorithm that identifies a small set of bundles to query from each bidder. However, a major limitation of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly for them. To address this, we propose a new ML-powered auction with interval bidding (i.e., where bidders submit upper and lower bounds for the queried bundles). To steer the auction towards an efficient allocation, we design a price-based refinement process asking bidders to tighten bounds on relevant bundles only, and we carefully integrate this refinement process into the ML-based query module. Our experiments show that our new auction with interval bidding achieves almost the same allocative efficiency as the prior auction design that required bidders to submit exact values. Despite only eliciting interval bids, our auction beats the well-known combinatorial clock auction in a realistically-sized domain.

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Section: Related Workmentioning

confidence: 99%

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

Beyeler

Brero

Lubin

et al. 2021

Proceedings of the 22nd ACM Conference on Economics and Computation

Self Cite

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“…We say that the clearing price θ (Brero et al 2019) supports the optimal allocation A * (•) with the maximum social welfare.…”

Section: Problem Formulationmentioning

confidence: 99%

Privacy preserving divisible double auction with a hybridized TEE-blockchain system

et al. 2021

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Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.

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“…This research goes back to Blum et al (2004) and Lahaie and Parkes (2004), who studied the relationship between computational learning theory and preference elicitation in CAs. More recently, Brero and Lahaie (2018) and Brero, Lahaie, and Seuken (2019) introduced a Bayesian CA where they integrated ML into a CA to achieve faster convergence. In a different strand of research, Dütting et al (2015;, Narasimhan, Agarwal, and Parkes (2016) and Golowich, Narasimhan, and Parkes (2018) used ML to directly learn a new mechanism (following the automated mechanism design paradigm).…”

Section: Machine Learning and Mechanism Designmentioning

confidence: 99%

Deep Learning—Powered Iterative Combinatorial Auctions

Weissteiner

Seuken

2020

AAAI

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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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Fast Iterative Combinatorial Auctions via Bayesian Learning

Cited by 13 publications

References 16 publications

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

Privacy preserving divisible double auction with a hybridized TEE-blockchain system

Deep Learning—Powered Iterative Combinatorial Auctions

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