2,942 research outputs found
A Lotting Method for Electronic Reverse Auctions
An increasing number of commercial companies are using online reverse auctions for their sourcing activities. In reverse auctions, multiple suppliers bid for a contract from a buyer for selling goods and/or services. Usually, the buyer has to procure multiple items, which are typically divided into lots for auctioning purposes. By steering the composition of the lots, a buyer can increase the attractiveness of its lots for thesuppliers, which can then make more competitive offers, leading to larger savings for the procuring party. In this paper, a clustering-based heuristic lotting method is proposed for reverse auctions. Agglomerative clustering is used for determining the items that will be put in the same lot. A suitable metric is defined, which allows the procurer to incorporate various approaches to lotting. The proposed lotting method has been tested for the procurement activities of a consumer packaged goods company. The results indicate that the proposed strategy leads to 2-3% savings, while the procurement experts confirm that the lots determined by the proposed method are acceptable given the procurement goals.e-commerce;reverse auctions;hierarchical clustering;lotting;e-procurement
Complexity Theory, Game Theory, and Economics: The Barbados Lectures
This document collects the lecture notes from my mini-course "Complexity
Theory, Game Theory, and Economics," taught at the Bellairs Research Institute
of McGill University, Holetown, Barbados, February 19--23, 2017, as the 29th
McGill Invitational Workshop on Computational Complexity.
The goal of this mini-course is twofold: (i) to explain how complexity theory
has helped illuminate several barriers in economics and game theory; and (ii)
to illustrate how game-theoretic questions have led to new and interesting
complexity theory, including recent several breakthroughs. It consists of two
five-lecture sequences: the Solar Lectures, focusing on the communication and
computational complexity of computing equilibria; and the Lunar Lectures,
focusing on applications of complexity theory in game theory and economics. No
background in game theory is assumed.Comment: Revised v2 from December 2019 corrects some errors in and adds some
recent citations to v1 Revised v3 corrects a few typos in v
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
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
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