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Recent years have witnessed huge advances in our knowledge and understanding of algorithmic techniques for Bayesian mechanism design problems. These include, for example, revenue maximization in settings where buyers have multi-dimensional preferences, optimization of non-linear objectives such as makespan, and generic reductions from mechanism design to algorithm design. However, a number of tantalizing questions remain un-solved.<\/jats:p>\n          <jats:p>This article is meant to serve as an introduction to Bayesian mechanism design for a novice, as well as a starting point for a broader literature search for an experienced researcher.<\/jats:p>","DOI":"10.1145\/2692375.2692378","type":"journal-article","created":{"date-parts":[[2014,11,26]],"date-time":"2014-11-26T14:51:56Z","timestamp":1417013516000},"page":"5-49","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Bayesian algorithmic mechanism design"],"prefix":"10.1145","volume":"13","author":[{"given":"Shuchi","family":"Chawla","sequence":"first","affiliation":[{"name":"University of Wisconsin - Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balasubramanian","family":"Sivan","sequence":"additional","affiliation":[{"name":"Microsoft Research"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,11,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2492002.2482557"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1137\/120878422"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2013.73"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2229012.2229017"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1566374.1566399"},{"key":"e_1_2_1_6_1","volume-title":"ACM Symp. on Discrete Algorithms. 596--604","author":"Azar P. 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