529 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
Bayesian Optimization-based Combinatorial Assignment
We study the combinatorial assignment domain, which includes combinatorial
auctions and course allocation. The main challenge in this domain is that the
bundle space grows exponentially in the number of items. To address this,
several papers have recently proposed machine learning-based preference
elicitation algorithms that aim to elicit only the most important information
from agents. However, the main shortcoming of this prior work is that it does
not model a mechanism's uncertainty over values for not yet elicited bundles.
In this paper, we address this shortcoming by presenting a Bayesian
Optimization-based Combinatorial Assignment (BOCA) mechanism. Our key technical
contribution is to integrate a method for capturing model uncertainty into an
iterative combinatorial auction mechanism. Concretely, we design a new method
for estimating an upper uncertainty bound that can be used as an acquisition
function to determine the next query to the agents. This enables the mechanism
to properly explore (and not just exploit) the bundle space during its
preference elicitation phase. We run computational experiments in several
spectrum auction domains to evaluate BOCA's performance. Our results show that
BOCA achieves higher allocative efficiency than state-of-the-art approaches
A theoretical and computational basis for CATNETS
The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing
Computing Perfect Bayesian Equilibria in Sequential Auctions
We present a best-response based algorithm for computing verifiable
-perfect Bayesian equilibria for sequential auctions with
combinatorial bidding spaces and incomplete information. Previous work has
focused only on computing Bayes-Nash equilibria for static single-round
auctions, which our work captures as a special case. Additionally, we prove an
upper bound on the utility loss of our approximate equilibria and
present an algorithm to efficiently compute based on the
immediate loss at each subgame. We evaluate the performance of our algorithm by
reproducing known results from several auctions previously introduced in the
literature, including a model of combinatorial split-award auctions used in
procurement.Comment: 12 pages, 8 figure
Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1
This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing
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