Topic
Bidding
About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.
Papers published on a yearly basis
Papers
More filters
•
TL;DR: In this article, the authors examine how extensively bidders signaled each other with retaliating bids and code bids in the DEF-block PCS spectrum auction held from August 1996 through January 1997.
Abstract: This paper describes the signaling that occurred in many of the FCC spectrum auctions. The FCC's simultaneous ascending auctions allowed bidders to bid on numerous communication licenses simultaneously, with bidding remaining open on all licenses until no bidder was willing to raise the bid on any license. Simultaneous open bidding allowed bidders to send messages to their rivals, telling them on which licenses to bid and which to avoid. This "code bidding" occurs when one bidder tags the last few digits of its bid with the market number of a related license. Such bids can help bidders coordinate a division of the licenses, and enforce the proposed division through targeted punishments. Often the meaning of a bid is clear without attaching a market number in the trailing digits. Such a "retaliating bid" need not end in a market number to warn off a rival from a contested market. We examine how extensively bidders signaled each other with retaliating bids and code bids in the DEF-block PCS spectrum auction held from August 1996 through January 1997. We find that only a small fraction of the bidders commonly used these signals. The price differences between those markets where signaling did and did not occur were negligible. However, bidders that used these collusive bidding strategies won more than 40% of the spectrum for sale and paid significantly less for their overall winnings, suggesting that the indirect losses from code bidding and retaliation may be large.
124 citations
••
01 Jan 2004TL;DR: Auctions have found widespread use in the last few years as a technique for supporting and automating negotiations on the Internet as discussed by the authors, and eBay now serves as a new selling channel for individuals, and small and big enterprises.
Abstract: Auctions have found widespread use in the last few years as a technique for supporting and automating negotiations on the Internet. For example, eBay now serves as a new selling channel for individuals, and small and big enterprises. Another use for auctions is for industrial procurement. In both these settings traditional auction mechanisms such as the English, Dutch, First (or Second) price Sealed-Bid auctions are now commonplace. These auctions types are useful for settings where there is a single unit of an item being bought/sold. However, since procurement problems are business-to-business they tend to be more complex and have led to the development and application of advanced auction types that allow for negotiations over multiple units of multiple items, and the configuration of the attributes of items. At the heart of auctions is the problem of decentralized resource allocation.
124 citations
••
TL;DR: In short, game-theoretic reasoning now scales to many large problems, outperforms the alternatives on those problems, and in some games beats the best humans.
Abstract: Game-theoretic solution concepts prescribe how rational parties should act, but to become operational the concepts need to be accompanied by algorithms. I will review the state of solving incomplete-information games. They encompass many practical problems such as auctions, negotiations, and security applications. I will discuss them in the context of how they have transformed computer poker. In short, game-theoretic reasoning now scales to many large problems, outperforms the alternatives on those problems, and in some games beats the best humans.
124 citations
••
17 Oct 2018TL;DR: The results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.
Abstract: Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.
124 citations
••
TL;DR: In this article, the authors consider a setting where several privately informed agents bid for a price and all bidders bear a cost of bidding that is an increasing function of their bids, and moreover, bids may be capped.
Abstract: We study contests where several privately informed agents bid for a price. All bidders bear a cost of bidding that is an increasing function of their bids, and, moreover, bids may be capped. We show that, regardless of the number of bidders, if agents have linear or concave cost functions then setting a bid cap is not profitable for a designer who wishes to maximize the average bid. On the other hand, if agents have convex cost functions (i.e. an increasing marginal cost) then affectively capping the bids is profitable for a designer facing a sufficiently large number of bidders.
124 citations