2,892 research outputs found
Explicit shading strategies for repeated truthful auctions
With the increasing use of auctions in online advertising, there has been a
large effort to study seller revenue maximization, following Myerson's seminal
work, both theoretically and practically. We take the point of view of the
buyer in classical auctions and ask the question of whether she has an
incentive to shade her bid even in auctions that are reputed to be truthful,
when aware of the revenue optimization mechanism.
We show that in auctions such as the Myerson auction or a VCG with reserve
price set as the monopoly price, the buyer who is aware of this information has
indeed an incentive to shade. Intuitively, by selecting the revenue maximizing
auction, the seller introduces a dependency on the buyers' distributions in the
choice of the auction. We study in depth the case of the Myerson auction and
show that a symmetric equilibrium exists in which buyers shade non-linearly
what would be their first price bid. They then end up with an expected payoff
that is equal to what they would get in a first price auction with no reserve
price.
We conclude that a return to simple first price auctions with no reserve
price or at least non-dynamic anonymous ones is desirable from the point of
view of both buyers, sellers and increasing transparency
A General Theory of Sample Complexity for Multi-Item Profit Maximization
The design of profit-maximizing multi-item mechanisms is a notoriously
challenging problem with tremendous real-world impact. The mechanism designer's
goal is to field a mechanism with high expected profit on the distribution over
buyers' values. Unfortunately, if the set of mechanisms he optimizes over is
complex, a mechanism may have high empirical profit over a small set of samples
but low expected profit. This raises the question, how many samples are
sufficient to ensure that the empirically optimal mechanism is nearly optimal
in expectation? We uncover structure shared by a myriad of pricing, auction,
and lottery mechanisms that allows us to prove strong sample complexity bounds:
for any set of buyers' values, profit is a piecewise linear function of the
mechanism's parameters. We prove new bounds for mechanism classes not yet
studied in the sample-based mechanism design literature and match or improve
over the best known guarantees for many classes. The profit functions we study
are significantly different from well-understood functions in machine learning,
so our analysis requires a sharp understanding of the interplay between
mechanism parameters and buyer values. We strengthen our main results with
data-dependent bounds when the distribution over buyers' values is
"well-behaved." Finally, we investigate a fundamental tradeoff in sample-based
mechanism design: complex mechanisms often have higher profit than simple
mechanisms, but more samples are required to ensure that empirical and expected
profit are close. We provide techniques for optimizing this tradeoff
The Value of Knowing Your Enemy
Many auction settings implicitly or explicitly require that bidders are
treated equally ex-ante. This may be because discrimination is philosophically
or legally impermissible, or because it is practically difficult to implement
or impossible to enforce. We study so-called {\em anonymous} auctions to
understand the revenue tradeoffs and to develop simple anonymous auctions that
are approximately optimal.
We consider digital goods settings and show that the optimal anonymous,
dominant strategy incentive compatible auction has an intuitive structure ---
imagine that bidders are randomly permuted before the auction, then infer a
posterior belief about bidder i's valuation from the values of other bidders
and set a posted price that maximizes revenue given this posterior.
We prove that no anonymous mechanism can guarantee an approximation better
than O(n) to the optimal revenue in the worst case (or O(log n) for regular
distributions) and that even posted price mechanisms match those guarantees.
Understanding that the real power of anonymous mechanisms comes when the
auctioneer can infer the bidder identities accurately, we show a tight O(k)
approximation guarantee when each bidder can be confused with at most k "higher
types". Moreover, we introduce a simple mechanism based on n target prices that
is asymptotically optimal and build on this mechanism to extend our results to
m-unit auctions and sponsored search
Optimal Crowdsourcing Contests
We study the design and approximation of optimal crowdsourcing contests.
Crowdsourcing contests can be modeled as all-pay auctions because entrants must
exert effort up-front to enter. Unlike all-pay auctions where a usual design
objective would be to maximize revenue, in crowdsourcing contests, the
principal only benefits from the submission with the highest quality. We give a
theory for optimal crowdsourcing contests that mirrors the theory of optimal
auction design: the optimal crowdsourcing contest is a virtual valuation
optimizer (the virtual valuation function depends on the distribution of
contestant skills and the number of contestants). We also compare crowdsourcing
contests with more conventional means of procurement. In this comparison,
crowdsourcing contests are relatively disadvantaged because the effort of
losing contestants is wasted. Nonetheless, we show that crowdsourcing contests
are 2-approximations to conventional methods for a large family of "regular"
distributions, and 4-approximations, otherwise.Comment: The paper has 17 pages and 1 figure. It is to appear in the
proceedings of ACM-SIAM Symposium on Discrete Algorithms 201
Core-competitive Auctions
One of the major drawbacks of the celebrated VCG auction is its low (or zero)
revenue even when the agents have high value for the goods and a {\em
competitive} outcome could have generated a significant revenue. A competitive
outcome is one for which it is impossible for the seller and a subset of buyers
to `block' the auction by defecting and negotiating an outcome with higher
payoffs for themselves. This corresponds to the well-known concept of {\em
core} in cooperative game theory.
In particular, VCG revenue is known to be not competitive when the goods
being sold have complementarities. A bottleneck here is an impossibility result
showing that there is no auction that simultaneously achieves competitive
prices (a core outcome) and incentive-compatibility.
In this paper we try to overcome the above impossibility result by asking the
following natural question: is it possible to design an incentive-compatible
auction whose revenue is comparable (even if less) to a competitive outcome?
Towards this, we define a notion of {\em core-competitive} auctions. We say
that an incentive-compatible auction is -core-competitive if its
revenue is at least fraction of the minimum revenue of a
core-outcome. We study the Text-and-Image setting. In this setting, there is an
ad slot which can be filled with either a single image ad or text ads. We
design an core-competitive randomized auction and an
competitive deterministic auction for the Text-and-Image
setting. We also show that both factors are tight
Auctions with Severely Bounded Communication
We study auctions with severe bounds on the communication allowed: each
bidder may only transmit t bits of information to the auctioneer. We consider
both welfare- and profit-maximizing auctions under this communication
restriction. For both measures, we determine the optimal auction and show that
the loss incurred relative to unconstrained auctions is mild. We prove
non-surprising properties of these kinds of auctions, e.g., that in optimal
mechanisms bidders simply report the interval in which their valuation lies in,
as well as some surprising properties, e.g., that asymmetric auctions are
better than symmetric ones and that multi-round auctions reduce the
communication complexity only by a linear factor
On Ascending Vickrey Auctions for Heterogeneous Objects
Vickrey auctions, multi-item auctions, combinatorial auctions,
On Revenue Monotonicity in Combinatorial Auctions
Along with substantial progress made recently in designing near-optimal
mechanisms for multi-item auctions, interesting structural questions have also
been raised and studied. In particular, is it true that the seller can always
extract more revenue from a market where the buyers value the items higher than
another market? In this paper we obtain such a revenue monotonicity result in a
general setting. Precisely, consider the revenue-maximizing combinatorial
auction for items and buyers in the Bayesian setting, specified by a
valuation function and a set of independent item-type
distributions. Let denote the maximum revenue achievable under
by any incentive compatible mechanism. Intuitively, one would expect that
if distribution stochastically dominates .
Surprisingly, Hart and Reny (2012) showed that this is not always true even for
the simple case when is additive. A natural question arises: Are these
deviations contained within bounds? To what extent may the monotonicity
intuition still be valid? We present an {approximate monotonicity} theorem for
the class of fractionally subadditive (XOS) valuation functions , showing
that if stochastically dominates under
where is a universal constant. Previously, approximate monotonicity was
known only for the case : Babaioff et al. (2014) for the class of additive
valuations, and Rubinstein and Weinberg (2015) for all subaddtive valuation
functions.Comment: 10 page
- …