3,320 research outputs found
Online learning in repeated auctions
Motivated by online advertising auctions, we consider repeated Vickrey
auctions where goods of unknown value are sold sequentially and bidders only
learn (potentially noisy) information about a good's value once it is
purchased. We adopt an online learning approach with bandit feedback to model
this problem and derive bidding strategies for two models: stochastic and
adversarial. In the stochastic model, the observed values of the goods are
random variables centered around the true value of the good. In this case,
logarithmic regret is achievable when competing against well behaved
adversaries. In the adversarial model, the goods need not be identical and we
simply compare our performance against that of the best fixed bid in hindsight.
We show that sublinear regret is also achievable in this case and prove
matching minimax lower bounds. To our knowledge, this is the first complete set
of strategies for bidders participating in auctions of this type
Randomized Revenue Monotone Mechanisms for Online Advertising
Online advertising is the main source of revenue for many Internet firms. A
central component of online advertising is the underlying mechanism that
selects and prices the winning ads for a given ad slot. In this paper we study
designing a mechanism for the Combinatorial Auction with Identical Items (CAII)
in which we are interested in selling identical items to a group of bidders
each demanding a certain number of items between and . CAII generalizes
important online advertising scenarios such as image-text and video-pod
auctions [GK14]. In image-text auction we want to fill an advertising slot on a
publisher's web page with either text-ads or a single image-ad and in
video-pod auction we want to fill an advertising break of seconds with
video-ads of possibly different durations.
Our goal is to design truthful mechanisms that satisfy Revenue Monotonicity
(RM). RM is a natural constraint which states that the revenue of a mechanism
should not decrease if the number of participants increases or if a participant
increases her bid.
[GK14] showed that no deterministic RM mechanism can attain PoRM of less than
for CAII, i.e., no deterministic mechanism can attain more than
fraction of the maximum social welfare. [GK14] also design a
mechanism with PoRM of for CAII.
In this paper, we seek to overcome the impossibility result of [GK14] for
deterministic mechanisms by using the power of randomization. We show that by
using randomization, one can attain a constant PoRM. In particular, we design a
randomized RM mechanism with PoRM of for CAII
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting
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