Algorithmic Pricing

Blum, Avrim

Title:

Algorithmic Pricing

dc.contributor.author	Blum, Avrim	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Algorithms, Randomness and Complexity Center	en_US
dc.contributor.corporatename	Carnegie-Mellon University	en_US
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Computer Science	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Industrial and Systems Engineering	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Mathematics	en_US
dc.date.accessioned	2013-04-25T13:03:32Z
dc.date.available	2013-04-25T13:03:32Z
dc.date.issued	2013-04-08
dc.description	ARC Distinguished Lecture presented on April 8, 2013 from 3:00 pm to 4:00 pm in room 1116 of the Klaus Advanced Computing Building.	en_US
dc.description	Avrim Blum is a prominent computer scientist who in 2007 was inducted as a Fellow of the Association for Computing Machinery "for contributions to learning theory and algorithms." Since 1991 he has been a professor of computer science at Carnegie Mellon University. His main work has been in the area of theoretical computer science, with particular activity in the fields of machine learning, computational learning theory, algorithmic game theory, and algorithms.	en_US
dc.description	Runtime: 52:36 minutes.	en_US
dc.description.abstract	Pricing and allocating goods to buyers with complex preferences in order to maximize some desired objective (e.g., social welfare or profit) is a central problem in Algorithmic Mechanism Design. In this talk I will discuss some particularly simple algorithms that are able to achieve surprisingly strong guarantees for a range of problems of this type. As one example, for the problem of pricing /resources/, modeled as goods having an increasing marginal extraction cost to the seller, a simple approach of pricing the /i/th unit of each good at a value equal to the anticipated extraction cost of the /2i/th unit gives a constant-factor approximation to social welfare for a wide range of cost curves and for arbitrary buyer valuation functions. I will also discuss simple algorithms with good approximation guarantees for revenue, as well as settings having an opposite character to resources, namely having economies of scale or decreasing marginal costs to the seller.	en_US
dc.format.extent	52:36 minutes
dc.identifier.uri	http://hdl.handle.net/1853/46837
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	Algorithms and Randomness Center (ARC) Distinguished Lecture
dc.subject	Algorithmic pricing	en_US
dc.subject	Pricing mechanisms	en_US
dc.subject	Resource allocation	en_US
dc.subject	Approximation algorithms	en_US
dc.subject	Social welfare	en_US
dc.subject	Profit	en_US
dc.subject	Combinatorial auctions	en_US
dc.subject	Economies of scale	en_US
dc.subject	eCommerce	en_US
dc.title	Algorithmic Pricing	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Algorithms and Randomness Center
local.contributor.corporatename	College of Computing
local.relation.ispartofseries	ARC Colloquium
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