{"id":324944,"date":"2016-11-20T20:24:41","date_gmt":"2016-11-21T04:24:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=324944"},"modified":"2018-10-16T20:54:19","modified_gmt":"2018-10-17T03:54:19","slug":"near-optimal-multi-unit-auctions-ordered-bidders","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/near-optimal-multi-unit-auctions-ordered-bidders\/","title":{"rendered":"Near-optimal multi-unit auctions with ordered bidders"},"content":{"rendered":"<div id=\"citationdetails\" class=\" x-tabs-top\">\n<div id=\"tab-body9\" class=\" x-tabs-body\">\n<div id=\"abstract\" class=\"ytab x-tabs-item-body\">\n<div class=\"tabbody\">\n<div>\n<p>We construct prior-free auctions with constant-factor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vector using supply-respecting prices that are nonincreasing in the bidder ordering and bounded above by the second-highest bid. As a consequence, our auctions are simultaneously near-optimal in a wide range of Bayesian multi-unit environments.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>We construct prior-free auctions with constant-factor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vector using supply-respecting prices that are nonincreasing in the bidder [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-324944","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us"],"msr_publishername":"","msr_edition":"EC '13 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Bhattacharya","user_id":0,"rest_url":false},{"type":"text","value":"Elias Koutsoupias","user_id":0,"rest_url":false},{"type":"user_nicename","value":"jakul","user_id":32147,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jakul"},{"type":"text","value":"Stefano Leonardi","user_id":0,"rest_url":false},{"type":"text","value":"Tim Roughgarden","user_id":0,"rest_url":false},{"type":"text","value":"Xiaoming 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