23,147 research outputs found
Online learning with graph-structured feedback against adaptive adversaries
We derive upper and lower bounds for the policy regret of -round online
learning problems with graph-structured feedback, where the adversary is
nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
and for strongly-observable and
weakly-observable graphs, respectively, based on analyzing a variant of the
Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
show that a matching lower bound of is achieved in
the case of full-information feedback. We also study the particular loss
structure of an oblivious adversary with switching costs, and show that in such
a setting, non-revealing strongly-observable feedback graphs achieve a lower
bound of , as well.Comment: This paper has been accepted to ISIT 201
The Bidder's Curse
We employ a novel approach to identify overbidding in the field. We compare auction prices to fixed prices for the same item on the same webpage. In detailed board-game data, 42 percent of auctions exceed the simultaneous fixed price. The result replicates in a broad cross-section of auctions (48 percent). A small fraction of overbidders, 17 percent, suffices to generate the overbidding. The observed behavior is inconsistent with rational behavior, even allowing for uncertainty and switching costs, since also the expected auction price exceeds the fixed price. Limited attention to outside options is most consistent with our results.
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