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          <dc:title>Stackelberg Network Pricing Games</dc:title>
          <dc:creator>Briest, Patrick</dc:creator>
          <dc:creator>Hoefer, Martin</dc:creator>
          <dc:creator>Krysta, Piotr</dc:creator>
          <dc:subject>Stackelberg Games</dc:subject>
          <dc:subject>Algorithmic Pricing</dc:subject>
          <dc:subject>Approximation Algorithms</dc:subject>
          <dc:subject>Inapproximability.</dc:subject>
          <dc:description>We study a multi-player one-round game termed Stackelberg Network&#13;
   Pricing Game, in which a leader can set prices for a subset of $m$&#13;
   priceable edges in a graph.  The other edges have a fixed cost.&#13;
   Based on the leader's decision one or more followers optimize a&#13;
   polynomial-time solvable combinatorial minimization problem and&#13;
   choose a minimum cost solution satisfying their requirements based&#13;
   on the fixed costs and the leader's prices.  The leader receives as&#13;
   revenue the total amount of prices paid by the followers for&#13;
   priceable edges in their solutions, and the problem is to find&#13;
   revenue maximizing prices.  Our model extends several known pricing&#13;
   problems, including single-minded and unit-demand pricing, as well&#13;
   as Stackelberg pricing for certain follower problems like shortest&#13;
   path or minimum spanning tree.  Our first main result is a tight&#13;
   analysis of a single-price algorithm for the single follower game,&#13;
   which provides a $(1+varepsilon) log m$-approximation for any&#13;
   $varepsilon &gt;0$.  This can be extended to provide a&#13;
   $(1+varepsilon )(log k + log m)$-approximation for the general&#13;
   problem and $k$ followers.  The latter result is essentially best&#13;
   possible, as the problem is shown to be hard to approximate within&#13;
   $mathcal{O(log^varepsilon k + log^varepsilon m)$.  If&#13;
   followers have demands, the single-price algorithm provides a&#13;
   $(1+varepsilon )m^2$-approximation, and the problem is hard to&#13;
   approximate within $mathcal{O(m^varepsilon)$ for some&#13;
   $varepsilon &gt;0$.  Our second main result is a polynomial time&#13;
   algorithm for revenue maximization in the special case of&#13;
   Stackelberg bipartite vertex cover, which is based on non-trivial&#13;
   max-flow and LP-duality techniques.  Our results can be extended to&#13;
   provide constant-factor approximations for any constant number of&#13;
   followers.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Patrick Briest and Martin Hoefer and Piotr Krysta</dc:contributor>
          <dc:date>2008</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 1, 25th International Symposium on Theoretical Aspects of Computer Science (2008)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/LIPIcs.STACS.2008.1340</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-13406</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2008.1340</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by-nd/3.0/legalcode</dc:rights>
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