Campus Event Calendar
Event Entry
What and Who
Optimality and Approximation in Revenue-Maximizing Auctions
Yiannis Giannakopoulos
Technical University of Munich
AG1 Advanced Mini-Course
Yiannis is Postdoctoral Researcher in the Chair of Operations Research at the Department of Mathematics of TU Munich, headed by Prof. Andreas Schulz. Before going to Munich, he was a postdoc with George Christodoulou at the Computer Science department and the Economics and Cmputations group of the University of Liverpool.
He completed his DPhil (aka PhD) in 2015 at the Computer Science department of the University of Oxford, advised by Elias Koutsoupias, where he was also a member of St Anne’s College. He holds an undergraduate degree in Mathematics and an MSc in Logic, Algorithms and Computation, both from the University of Athens.
Web page: https://ygiannak.gitlab.io/
AG 1, MMCI
AG Audience
English
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Date, Time and Location
Thursday, 22 November 2018
13:00
45 Minutes
E1 4
024
Saarbrücken
Abstract
In this talk we will present a general duality-theory framework for revenue
maximization in additive Bayesian auctions involving many bidders, multiple items
and arbitrary joint value distributions. Although the single-item case has been
resolved in a very elegant way by the seminal work of Myerson [1981], optimal
solutions involving more items still remain elusive. The framework extends linear
programming duality and complementarity to constraints with partial derivatives.
The dual system reveals the geometric nature of the problem and highlights its
connection with the theory of bipartite graph matchings. We will demonstrate the
power of the framework by applying it to special single-bidder settings with
independent item valuations drawn from various distributions of interest, to design
both exact and approximately optimal auctions.
We will also briefly discuss the standard Bayesian auction setting, where multiple
bidders have i.i.d. valuations for a single item, showing that for the natural class
of Monotone Hazard Rate (MHR) distributions, offering the same, take-it-or-leave-it
price to all bidders achieves an (asymptotically) optimal revenue.
Some of the related papers can be found in the following links:
https://arxiv.org/abs/1404.2329
https://arxiv.org/abs/1510.03399
https://arxiv.org/abs/1404.2832
https://arxiv.org/abs/1810.00800
maximization in additive Bayesian auctions involving many bidders, multiple items
and arbitrary joint value distributions. Although the single-item case has been
resolved in a very elegant way by the seminal work of Myerson [1981], optimal
solutions involving more items still remain elusive. The framework extends linear
programming duality and complementarity to constraints with partial derivatives.
The dual system reveals the geometric nature of the problem and highlights its
connection with the theory of bipartite graph matchings. We will demonstrate the
power of the framework by applying it to special single-bidder settings with
independent item valuations drawn from various distributions of interest, to design
both exact and approximately optimal auctions.
We will also briefly discuss the standard Bayesian auction setting, where multiple
bidders have i.i.d. valuations for a single item, showing that for the natural class
of Monotone Hazard Rate (MHR) distributions, offering the same, take-it-or-leave-it
price to all bidders achieves an (asymptotically) optimal revenue.
Some of the related papers can be found in the following links:
https://arxiv.org/abs/1404.2329
https://arxiv.org/abs/1510.03399
https://arxiv.org/abs/1404.2832
https://arxiv.org/abs/1810.00800
Contact
Alkmini Sgouritsa
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Alkmini Sgouritsa, 11/21/2018 17:27
Alkmini Sgouritsa, 11/07/2018 09:21
Alkmini Sgouritsa, 11/02/2018 12:58 -- Created document.
Alkmini Sgouritsa, 11/07/2018 09:21
Alkmini Sgouritsa, 11/02/2018 12:58 -- Created document.