14,654 research outputs found
Learning Utilities and Equilibria in Non-Truthful Auctions
In non-truthful auctions, agents' utility for a strategy depends on the
strategies of the opponents and also the prior distribution over their private
types; the set of Bayes Nash equilibria generally has an intricate dependence
on the prior. Using the First Price Auction as our main demonstrating example,
we show that samples from the prior with agents
suffice for an algorithm to learn the interim utilities for all monotone
bidding strategies. As a consequence, this number of samples suffice for
learning all approximate equilibria. We give almost matching (up to polylog
factors) lower bound on the sample complexity for learning utilities. We also
consider settings where agents must pay a search cost to discover their own
types. Drawing on a connection between this setting and the first price
auction, discovered recently by Kleinberg et al. (2016), we show that samples suffice for utilities and equilibria to be estimated
in a near welfare-optimal descending auction in this setting. En route, we
improve the sample complexity bound, recently obtained by Guo et al. (2019),
for the Pandora's Box problem, which is a classical model for sequential
consumer search
Vickrey Auctions for Irregular Distributions
The classic result of Bulow and Klemperer \cite{BK96} says that in a
single-item auction recruiting one more bidder and running the Vickrey auction
achieves a higher revenue than the optimal auction's revenue on the original
set of bidders, when values are drawn i.i.d. from a regular distribution. We
give a version of Bulow and Klemperer's result in settings where bidders'
values are drawn from non-i.i.d. irregular distributions. We do this by
modeling irregular distributions as some convex combination of regular
distributions. The regular distributions that constitute the irregular
distribution correspond to different population groups in the bidder
population. Drawing a bidder from this collection of population groups is
equivalent to drawing from some convex combination of these regular
distributions. We show that recruiting one extra bidder from each underlying
population group and running the Vickrey auction gives at least half of the
optimal auction's revenue on the original set of bidders
Optimal pricing using online auction experiments: A P\'olya tree approach
We show how a retailer can estimate the optimal price of a new product using
observed transaction prices from online second-price auction experiments. For
this purpose we propose a Bayesian P\'olya tree approach which, given the
limited nature of the data, requires a specially tailored implementation.
Avoiding the need for a priori parametric assumptions, the P\'olya tree
approach allows for flexible inference of the valuation distribution, leading
to more robust estimation of optimal price than competing parametric
approaches. In collaboration with an online jewelry retailer, we illustrate how
our methodology can be combined with managerial prior knowledge to estimate the
profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimal Auctions for Correlated Buyers with Sampling
Cr\'emer and McLean [1985] showed that, when buyers' valuations are drawn
from a correlated distribution, an auction with full knowledge on the
distribution can extract the full social surplus. We study whether this
phenomenon persists when the auctioneer has only incomplete knowledge of the
distribution, represented by a finite family of candidate distributions, and
has sample access to the real distribution. We show that the naive approach
which uses samples to distinguish candidate distributions may fail, whereas an
extended version of the Cr\'emer-McLean auction simultaneously extracts full
social surplus under each candidate distribution. With an algebraic argument,
we give a tight bound on the number of samples needed by this auction, which is
the difference between the number of candidate distributions and the dimension
of the linear space they span
Sequential Posted Price Mechanisms with Correlated Valuations
We study the revenue performance of sequential posted price mechanisms and
some natural extensions, for a general setting where the valuations of the
buyers are drawn from a correlated distribution. Sequential posted price
mechanisms are conceptually simple mechanisms that work by proposing a
take-it-or-leave-it offer to each buyer. We apply sequential posted price
mechanisms to single-parameter multi-unit settings in which each buyer demands
only one item and the mechanism can assign the service to at most k of the
buyers. For standard sequential posted price mechanisms, we prove that with the
valuation distribution having finite support, no sequential posted price
mechanism can extract a constant fraction of the optimal expected revenue, even
with unlimited supply. We extend this result to the the case of a continuous
valuation distribution when various standard assumptions hold simultaneously.
In fact, it turns out that the best fraction of the optimal revenue that is
extractable by a sequential posted price mechanism is proportional to ratio of
the highest and lowest possible valuation. We prove that for two simple
generalizations of these mechanisms, a better revenue performance can be
achieved: if the sequential posted price mechanism has for each buyer the
option of either proposing an offer or asking the buyer for its valuation, then
a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d
denotes the degree of dependence of the valuations, ranging from complete
independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we
generalize the sequential posted price mechanisms further, such that the
mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer
that depends on the valuations of all buyers except i's, we prove that a
constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be
extracted.Comment: 29 pages, To appear in WINE 201
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