299 research outputs found
Fast Iterative Combinatorial Auctions via Bayesian Learning
Iterative combinatorial auctions (CAs) are often used in multi-billion dollar
domains like spectrum auctions, and speed of convergence is one of the crucial
factors behind the choice of a specific design for practical applications. To
achieve fast convergence, current CAs require careful tuning of the price
update rule to balance convergence speed and allocative efficiency. Brero and
Lahaie (2018) recently introduced a Bayesian iterative auction design for
settings with single-minded bidders. The Bayesian approach allowed them to
incorporate prior knowledge into the price update algorithm, reducing the
number of rounds to convergence with minimal parameter tuning. In this paper,
we generalize their work to settings with no restrictions on bidder valuations.
We introduce a new Bayesian CA design for this general setting which uses Monte
Carlo Expectation Maximization to update prices at each round of the auction.
We evaluate our approach via simulations on CATS instances. Our results show
that our Bayesian CA outperforms even a highly optimized benchmark in terms of
clearing percentage and convergence speed.Comment: 9 pages, 2 figures, AAAI-1
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A Modular Framework for Iterative Combinatorial Auctions
We describe a modular elicitation framework for iterative combinatorial auctions. The framework includes proxy agents, each of which can adopt an individualized bidding language to represent partial value information of a bidder. The framework leverages algorithms from query learning to elicit value information via bids as the auction progresses. The approach reduces the multi-agent elicitation problem to isolated, single-agent learning problems, with competitive equilibrium prices used to facilitate auction clearing even without complete learning.Engineering and Applied Science
Fourier Analysis-based Iterative Combinatorial Auctions
Recent advances in Fourier analysis have brought new tools to efficiently
represent and learn set functions. In this paper, we bring the power of Fourier
analysis to the design of combinatorial auctions (CAs). The key idea is to
approximate bidders' value functions using Fourier-sparse set functions, which
can be computed using a relatively small number of queries. Since this number
is still too large for real-world CAs, we propose a new hybrid design: we first
use neural networks to learn bidders' values and then apply Fourier analysis to
the learned representations. On a technical level, we formulate a Fourier
transform-based winner determination problem and derive its mixed integer
program formulation. Based on this, we devise an iterative CA that asks
Fourier-based queries. We experimentally show that our hybrid ICA achieves
higher efficiency than prior auction designs, leads to a fairer distribution of
social welfare, and significantly reduces runtime. With this paper, we are the
first to leverage Fourier analysis in CA design and lay the foundation for
future work in this area
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More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
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.Engineering and Applied Science
Machine Learning-powered Course Allocation
We introduce a machine learning-powered course allocation mechanism.
Concretely, we extend the state-of-the-art Course Match mechanism with a
machine learning-based preference elicitation module. In an iterative,
asynchronous manner, this module generates pairwise comparison queries that are
tailored to each individual student. Regarding incentives, our machine
learning-powered course match (MLCM) mechanism retains the attractive
strategyproofness in the large property of Course Match. Regarding welfare, we
perform computational experiments using a simulator that was fitted to
real-world data. Our results show that, compared to Course Match, MLCM
increases average student utility by 4%-9% and minimum student utility by
10%-21%, even with only ten comparison queries. Finally, we highlight the
practicability of MLCM and the ease of piloting it for universities currently
using Course Match
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