2016/07/26 by Tim Roughgarden, Vasilis Syrgkanis, Roughgarden, Tim +4 · 3 citations
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.GT #cs.LG
paper · pdf · doi:10.48550/arxiv.1607.07684
arxiv created 2016/07/26 · openalex publication_date 2016/07/26 · arxiv updated 2016/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This survey outlines a general and modular theory for proving approximation guarantees for equilibria of auctions in complex settings. This theory complements traditional economic techniques, which generally focus on exact and optimal solutions and are accordingly limited to relatively stylized settings. We highlight three user-friendly analytical tools: smoothness-type inequalities, which immediately yield approximation guarantees for many auction formats of interest in the special case of complete information and deterministic strategies; extension theorems, which extend such guarantees to randomized strategies, no-regret learning outcomes, and incomplete-information settings; and composition theorems, which extend such guarantees from simpler to more complex auctions. Combining these tools yields tight worst-case approximation guarantees for the equilibria of many widely-used auction formats.