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.
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TL;DR: In this article, a model of adaptive learning was proposed to capture the bidding patterns evident among human subjects in experimental auctions and provided a variety of insights into the nature of learning across different auction institutions.
160 citations
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TL;DR: In this paper, the authors consider situations where multiple objects are auctioned simultaneously by means of a second-price, sealed-bid auction, and characterize an equilibrium that is symmetric among the global bidders and show that the addition of bi-dders often leads to less aggressive bidding.
Abstract: Motivated by recent auctions of licenses for the radio frequency spec trum, we consider situations where multiple objects are auctioned simultaneousl y by means of a second-price, sealed-bid auction. For some buyers, called globa l bidders, the value of multiple objects exceeds the sum of the objects' values separately. Others, called local bidders, are interested in only one object. I n a simple independent private values setting, we (a) characterize an equilibri um that is symmetric among the global bidders; (b) show that the addition of bi dders often leads to less aggressive bidding; and (c) compare the revenues obta ined from the simultaneous auction to those from its sequential counterpart.
159 citations
10 Dec 2002
TL;DR: A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs and it was found that the proposed neural networks were capable of forecasting L MP values efficiently.
Abstract: Bidding competition is one of the main transaction approaches in deregulated electricity markets. Locational marginal prices (LMPs) resulting from bidding competition represent electricity values at nodes or in areas. A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs. The recurrent neural network (RNN) was addressed and the traditional NN-based on a backpropagation algorithm was also investigated for comparison. The FCM helped classify the load levels into three clusters. Individual RNNs according to three load clusters were developed for forecasting LMPs. These RNNs were trained/ validated and tested with historical data from the PJM (Pennsylvania, New Jersey, and Maryland) power system. It was found that the proposed neural networks were capable of forecasting LMP values efficiently.
159 citations
TL;DR: In this article, the authors used data from California auctions for road construction contracts, where small businesses receive a 5 percent bid preference in auctions for projects using only state funds and no preferential treatment on projects using federal aid.
159 citations
TL;DR: A game that requires agents to manage the assembly of PCs, while competing with one another both for customer orders and for key components is designed to promote the research and evaluation of automated solutions to supply chain management under realistic conditions.
158 citations