The Experts below are selected from a list of 5871 Experts worldwide ranked by ideXlab platform
Wolfgang Leininger - One of the best experts on this subject based on the ideXlab platform.
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Expected Revenue of all pay and first price sealed bid auctions with affiliated signals
Journal of Economics, 1995Co-Authors: Erwin Amann, Wolfgang LeiningerAbstract:In the general symmetric auction framework of Milgrom and Weber (1982) it is shown—as a new manifestation of thelinkage principle—that the all-pay sealed-bid auction yieldshigher Expected Revenue than the standard first-price sealed-bid auction. This raises the question why sealed-bid auctions of the standard first-price variety are observed in practice whereas the all-pay variety is not.
Y. Narahari - One of the best experts on this subject based on the ideXlab platform.
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An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms
IEEE Transactions on Automation Science and Engineering, 2009Co-Authors: Dinesh Garg, Y. NarahariAbstract:In this paper, we first describe a framework to model the sponsored search auction on the Web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction - generalized second price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engine's Expected Revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the Expected Revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity.
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Design of an optimal auction for sponsored search auction
The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing E-Commerce and E-Serv, 2007Co-Authors: Dinesh Garg, Y. Narahari, Siva Sankar ReddyAbstract:In this paper, we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we design a novel auction which we call the OPT (optimal) auction. The OPT mechanism maximizes the search engine's Expected Revenue while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We show that the OPT mechanism is superior to two of the most commonly used mechanisms for sponsored search namely (1) GSP (Generalized Second Price) and (2) VCG (Vickrey-Clarke-Groves). We then show an important Revenue equivalence result that the Expected Revenue earned by the search engine is the same for all the three mechanisms provided the advertisers are symmetric and the number of sponsored slots is strictly less than the number of advertisers.
Dinesh Garg - One of the best experts on this subject based on the ideXlab platform.
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An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms
IEEE Transactions on Automation Science and Engineering, 2009Co-Authors: Dinesh Garg, Y. NarahariAbstract:In this paper, we first describe a framework to model the sponsored search auction on the Web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction - generalized second price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engine's Expected Revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the Expected Revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity.
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Design of an optimal auction for sponsored search auction
The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing E-Commerce and E-Serv, 2007Co-Authors: Dinesh Garg, Y. Narahari, Siva Sankar ReddyAbstract:In this paper, we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we design a novel auction which we call the OPT (optimal) auction. The OPT mechanism maximizes the search engine's Expected Revenue while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We show that the OPT mechanism is superior to two of the most commonly used mechanisms for sponsored search namely (1) GSP (Generalized Second Price) and (2) VCG (Vickrey-Clarke-Groves). We then show an important Revenue equivalence result that the Expected Revenue earned by the search engine is the same for all the three mechanisms provided the advertisers are symmetric and the number of sponsored slots is strictly less than the number of advertisers.
Erwin Amann - One of the best experts on this subject based on the ideXlab platform.
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Expected Revenue of all pay and first price sealed bid auctions with affiliated signals
Journal of Economics, 1995Co-Authors: Erwin Amann, Wolfgang LeiningerAbstract:In the general symmetric auction framework of Milgrom and Weber (1982) it is shown—as a new manifestation of thelinkage principle—that the all-pay sealed-bid auction yieldshigher Expected Revenue than the standard first-price sealed-bid auction. This raises the question why sealed-bid auctions of the standard first-price variety are observed in practice whereas the all-pay variety is not.
Yutung Liang - One of the best experts on this subject based on the ideXlab platform.
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a dynamic programming algorithm based on Expected Revenue approximation for the network Revenue management problem
Transportation Research Part E-logistics and Transportation Review, 2011Co-Authors: Kuancheng Huang, Yutung LiangAbstract:Since American Airlines successfully applied Revenue management (RM) to raise its Revenue, RM has become a common technique in the airline industry. Due to the current hub-and-spoke operation of the airline industry, the focus of RM research has shifted from the traditional single-leg problem to the network-type problem. The mainstream approaches, bid price and virtual nesting, are faced with some limitations such as inaccuracy due to their suboptimal nature and operation interruption caused by the required updates. This study developed an algorithm to generate a seat control policy by approximating the Expected Revenue function in a dynamic programming (DP) model. In order to deal with the issue of dimensionality for the DP model in a network context, this study used a suitable parameterized function and a sampling concept to achieve the approximation. In the numerical experiment, the objective function value of the developed algorithm was very close to the one achieved by the optimal control. We believe that this approach can serve as an alternative to the current mainstream approaches for the network RM problem for airlines and will provide an inspiring concept for other types of multi-resource RM problems.
