Web and Internet Economics

Web and Internet Economics
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10th International Conference, WINE 2014, Beijing, China, December 14-17, 2014, Proceedings
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Artikel-Nr:
9783319131290
Veröffentl:
2014
Einband:
eBook
Seiten:
500
Autor:
Tie-Yan Liu
eBook Typ:
PDF
eBook Format:
eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

This book constitutes the thoroughly refereed conference proceedings of the 10th International Conference on Web and Internet Economics, WINE 2014, held in Beijing, China, in December 2014. The 32 regular and 13 short papers were carefully reviewed and selected from 107 submissions and cover results on incentives and computation in theoretical computer science, artificial intelligence, and microeconomics.
This book constitutes the thoroughly refereed conference proceedings of the 10th International Conference on Web and Internet Economics, WINE 2014, held in Beijing, China, in December 2014. The 32 regular and 13 short papers were carefully reviewed and selected from 107 submissions and cover results on incentives and computation in theoretical computer science, artificial intelligence, and microeconomics.
Cake Cutting Algorithms for Piecewise Constant and Piecewise Uniform Valuations.- Bounding the Potential Function in Congestion Games and Approximate Pure Nash Equilibria.- Limiting Price Discrimination when Selling Products with Positive Network Externalities.- Computing Approximate Nash Equilibria in Polymatrix Games.- Optimal Cost-Sharing in Weighted Congestion Games.- Truthful Multi-unit Procurements with Budgets.- The Shapley Value in Knapsack Budgeted Games.- Fast Convex Decomposition for Truthful Social Welfare Approximation.- A Near-Optimal Mechanism for Impartial Selection.- Value-based Network Externalities and Optimal Auction Design.- Matching Dynamics with Constraints.- Truthful Approximations to Range Voting.- Resource Competition on Integral Polymatroids.- PTAS for Minimax Approval Voting.- Biobjective Online Bipartite Matching.- Dynamic Reserve Prices for Repeated Auctions: Learning from Bids.

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