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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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BookDOI
01 Jan 2014
TL;DR: Game theory has been applied to a growing list of practical problems, from antitrust analysis to monetary policy; from the design of auction institutions to the structuring of incentives within firms; from patent races to dispute resolution.
Abstract: Game theory has been applied to a growing list of practical problems, from antitrust analysis to monetary policy; from the design of auction institutions to the structuring of incentives within firms; from patent races to dispute resolution. The purpose of Game Theory and Business Applications is to show how game theory can be used to model and analyze business decisions. The contents of this revised edition contain a wide variety of business functions – from accounting to operations, from marketing to strategy to organizational design. In addition, specific application areas include market competition, law and economics, bargaining and dispute resolution, and competitive bidding. All of these applications involve competitive decision settings, specifically situations where a number of economic agents in pursuit of their own self-interests and in accordance with the institutional “rules of the game” take actions that together affect all of their fortunes. As this volume demonstrates, game theory provides a compelling guide for analyzing business decisions and strategies.

77 citations

Journal ArticleDOI
TL;DR: The inverses of standard deviations are found to capture a sense of connection and based on this finding, a new training method to identify and eliminate unimportant input factors is developed and leads to significantly improved prediction performance with a smaller number of network parameters.
Abstract: In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input-output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should be deleted because they mislead local learning and result in poor predictions. Existing pruning methods for neural networks examine the significance of connections between neurons, and are not applicable to deleting center and standard deviation parameters in a GRBF network since those parameters bear no sense of significance of connection. In this paper, the inverses of standard deviations are found to capture a sense of connection, and based on this finding, a new training method to identify and eliminate unimportant input factors is developed. Numerical testing results from two classroom problems and from New England Market Clearing Price prediction show that the new training method leads to significantly improved prediction performance with a smaller number of network parameters.

77 citations

Journal ArticleDOI
TL;DR: The capability of the proposed methodology for probabilistic energy price forecast based on Bayesian deep learning techniques to achieve robust performances in out-of-sample conditions while providing forecast uncertainty indications is demonstrated.

77 citations

Journal ArticleDOI
TL;DR: This work analyzes the allocation of priority in queues via simple bidding mechanisms and shows how the convexity/concavity of the function expressing the costs of delay determines the queue discipline (i.e., shortest- processing-time-first (SPT), longest-processing- time- first (LPT) arising in a bidding equilibrium.
Abstract: We analyze the allocation of priority in queues via simple bidding mechanisms In our model, the stochastically arriving customers are privately informed about their own processing time They make bids upon arrival at a queue whose length is unobservable We consider two bidding schemes that differ in the definition of bids (these may reflect either total payments or payments per unit of time) and in the timing of payments (before or after service) In both schemes, a customer obtains priority over all customers, waiting in the queue or arriving while he is waiting, who make lower bids Our main results show how the convexity/concavity of the function expressing the costs of delay determines the queue discipline (ie, shortest-processing-time-first (SPT), longest-processing-time-first (LPT)) arising in a bidding equilibrium

77 citations

Journal ArticleDOI
TL;DR: This work proposes a new methodology based on a graph coloring model and a tabu search algorithm for determining if the problem contains at least one feasible solution and shows how to combine the proposed approach with a heuristic sequential scheduling method that uses column generation and branch-and-bound techniques.

76 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023566
20221,134
2021637
2020708
2019830