[Colloquium] Talk by Jason Hartline on Wednesday, February 21st, 2007

[Margery Ishmael]

DEPARTMENT OF COMPUTER SCIENCE - TALK

Date: Wednesday, February 21, 2007
Time: 2:30 p.m.
Place: Ryerson 251 (1100 E. 58th St.)

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Speaker: JASON HARTLINE, Microsoft Research

Title: Economics, Algorithms, and the Internet.

Short abstract:

I will give a general approach to designing optimal mechanisms, e.g.,
maximizing profit or social welfare.  This approach is through
'optimal pricing' instead of 'optimal allocating' which enables
us to relax two common assumptions in the economics literature on
mechanism design.  I do not assume that agent preferences come from
a known distribution, nor do I assume that agent utilities are
quasi-linear.

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Detailed abstract:

The Internet has emerged the single most important platform for
resource sharing among parties with diverse and selfish interests
and is the most interesting economics system of our time.  As
rapidly as new Internet systems are developed and deployed,
Internet users find ways to exploit them for their own benefit
in ways not intended by the designers.  Prevalent undesirable
examples of this phenomenon include spam in email systems,
bid-sniping and shill bidding in eBay's auction marketplace,
free-loading in file-sharing  networks, and click-fraud in
Internet advertising.  The existence of these economic bugs
brings to fore the urgent problem of developing a principled
approach to designing systems where exploits are not
economically viable.  This is the general question addressed
by the research area of 'algorithmic mechanism design'.

Techniques from algorithms are very well suited for addressing
both new and traditional economic questions.  In this talk I will
highlight three of these: computation, reduction, and approximation.
I will introduce the research area of 'algorithmic pricing' which
looks at the  question of computing a pricing (e.g., on items in
a supermarket) given a known demand (e.g., the preferences of
shoppers).  I will reduce algorithmic mechanism design, where the
demand is unknown, to algorithmic pricing.  The reduction I will
give is based on random sampling, it preserves approximations, and
its analysis makes the connection between mechanism design and
machine learning theory concrete.  This approach is very generally
applicable; however, in this talk I will focus on the economic
objective of profit maximization.


***The talk will be followed by refreshments in Ryerson 255***

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Hosts:  Lance Fortnow & Laci Babai

People in need of assistance should call 773-834-8977 in advance.

For information on future CS talks: http://www.cs.uchicago.edu/events


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