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DTSTART:20181028T030000
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DTSTART:20180325T020000
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UID:calendar.13538.field_data.0@www.corsodrupal.uniroma1.it
DTSTAMP:20240422T184704Z
CREATED:20181012T164021Z
DESCRIPTION:In this talk we will briefly overview some of our recent result
s in the area of optimalauctions: We will first study a standard Bayesian
auction setting\, where multiple bidders havei.i.d. valuations for a singl
e item\, showing that for the natural class of MonotoneHazard Rate (MHR) d
istributions\, offering the same\, take-it-or-leave-it price to allbidders
achieves an (asymptotically) optimal revenue. We will then present a gene
ralduality-theory framework for revenue maximization in additive Bayesian
auctionsinvolving many bidders\, multiple items and arbitrary joint value
distributions. Wewill demonstrate the power of the framework by applying i
t to special single-biddersettings with independent item valuations drawn
from various distributions ofinterest\, to design both exact and approxima
tely optimal auctions. Previous exactsolutions were essentially only known
for up to two items and for a very limitednumber of specific distribution
s. Finally\, we will study a dynamic market settingwhere an intermediary i
nteracts with an unknown large sequence of agents that can beeither seller
s or buyers: their identities\, as well as the sequence length\, aredecide
d in an adversarial\, online way. The intermediary has some prior\,distrib
utional knowledge of the agents' values for the items\, and uses aposted-p
rice selling mechanisms. Some of the related papers have appeared in EC'14
\, ICALP '15 and '17\, WINE'18\, andthey can be found in the following lin
ks: https://arxiv.org/abs/1404.2329https://arxiv.org/abs/1510.03399https:/
/arxiv.org/abs/1703.09279https://arxiv.org/abs/1810.00800
DTSTART;TZID=Europe/Paris:20181016T120000
DTEND;TZID=Europe/Paris:20181016T120000
LAST-MODIFIED:20200528T093127Z
LOCATION:Room B203\, II floor
SUMMARY:Optimality\, Approximation and Robustness in Auctions - Yiannis Gia
nnakopoulos
URL;TYPE=URI:http://www.corsodrupal.uniroma1.it/node/13538
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