2,030 research outputs found
Constrained Signaling in Auction Design
We consider the problem of an auctioneer who faces the task of selling a good
(drawn from a known distribution) to a set of buyers, when the auctioneer does
not have the capacity to describe to the buyers the exact identity of the good
that he is selling. Instead, he must come up with a constrained signalling
scheme: a (non injective) mapping from goods to signals, that satisfies the
constraints of his setting. For example, the auctioneer may be able to
communicate only a bounded length message for each good, or he might be legally
constrained in how he can advertise the item being sold. Each candidate
signaling scheme induces an incomplete-information game among the buyers, and
the goal of the auctioneer is to choose the signaling scheme and accompanying
auction format that optimizes welfare. In this paper, we use techniques from
submodular function maximization and no-regret learning to give algorithms for
computing constrained signaling schemes for a variety of constrained signaling
problems
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
Designing and Executing a Fair Revlon Auction
The author analyzes the role of corporate boards of directors during takeover and control transactions, specifically in regards to auctions. Courts have consistently considered unfair auction attempts in light of the importance of the business judgment rule. The author examines Delaware case law and highlights the Revlon case, which holds that once an auction begins, the board’s duty shifts from preservation of the corporate entity to maximization of value shareholders will receive from the sale. The author argues that a good understanding of auction theory will not only give courts a better perspective through which to examine directors’ actions but also will give directors more information on how to run auctions and respond to bids
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