{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:04:52Z","timestamp":1775243092510,"version":"3.50.1"},"reference-count":94,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,6,13]]},"abstract":"<jats:p>Systems wherein strategic agents compete for limited resources are ubiquitous: the economy, computer networks, social networks, congestion networks, nature, etc. Assuming the agents' preferences are drawn from a distribution, which is a reasonable assumption for small mechanisms in a large system, Bayesian mechanism design governs the design and analysis of these systems.<\/jats:p>\n                  <jats:p>This article surveys the classical economic theory of Bayesian mechanism design and recent advances from the perspective of algorithms and approximation. Classical economics gives simple characterizations of Bayes-Nash equilibrium and optimal mechanisms when the agents' preferences are linear and single-dimensional. The mechanisms it predicts are often complex and overly dependent on details of the model. Approximation complements this theory and suggests that simple and less-detail-dependent mechanisms can be nearly optimal. Furthermore, techniques from approximation and algorithms can be used to describe good mechanisms beyond the single-dimensional, linear model of agent preferences.<\/jats:p>","DOI":"10.1561\/0400000045","type":"journal-article","created":{"date-parts":[[2013,6,13]],"date-time":"2013-06-13T05:57:54Z","timestamp":1371103074000},"page":"143-263","source":"Crossref","is-referenced-by-count":30,"title":["Bayesian Mechanism Design"],"prefix":"10.1561","volume":"8","author":[{"given":"Jason D.","family":"Hartline","sequence":"first","affiliation":[{"name":"Northwestern University , Evanston, 60208,","place":["USA"]}]}],"member":"140","published-online":{"date-parts":[[2013,6,13]]},"reference":[{"issue":"1","key":"2026040314242665300_ref001","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1287\/opre.1110.1011","article-title":"Price of correlations in stochastic optimization","volume":"60","author":"Agrawal","year":"2012","journal-title":"Operations 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