11 research outputs found
Constant-Competitive Prior-Free Auction with Ordered Bidders
A central problem in Microeconomics is to design auctions with good revenue
properties. In this setting, the bidders' valuations for the items are private
knowledge, but they are drawn from publicly known prior distributions. The goal
is to find a truthful auction (no bidder can gain in utility by misreporting
her valuation) that maximizes the expected revenue.
Naturally, the optimal-auction is sensitive to the prior distributions. An
intriguing question is to design a truthful auction that is oblivious to these
priors, and yet manages to get a constant factor of the optimal revenue. Such
auctions are called prior-free.
Goldberg et al. presented a constant-approximate prior-free auction when
there are identical copies of an item available in unlimited supply, bidders
are unit-demand, and their valuations are drawn from i.i.d. distributions. The
recent work of Leonardi et al. [STOC 2012] generalized this problem to non
i.i.d. bidders, assuming that the auctioneer knows the ordering of their
reserve prices. Leonardi et al. proposed a prior-free auction that achieves a
approximation. We improve upon this result, by giving the first
prior-free auction with constant approximation guarantee.Comment: The same result has been obtained independently by E. Koutsoupias, S.
Leonardi and T. Roughgarde
Optimal Auctions vs. Anonymous Pricing
For selling a single item to agents with independent but non-identically
distributed values, the revenue optimal auction is complex. With respect to it,
Hartline and Roughgarden (2009) showed that the approximation factor of the
second-price auction with an anonymous reserve is between two and four. We
consider the more demanding problem of approximating the revenue of the ex ante
relaxation of the auction problem by posting an anonymous price (while supplies
last) and prove that their worst-case ratio is e. As a corollary, the
upper-bound of anonymous pricing or anonymous reserves versus the optimal
auction improves from four to . We conclude that, up to an factor,
discrimination and simultaneity are unimportant for driving revenue in
single-item auctions.Comment: 19 pages, 6 figures, To appear in 56th Annual IEEE Symposium on
Foundations of Computer Science (FOCS 2015
Budget Constrained Auctions with Heterogeneous Items
In this paper, we present the first approximation algorithms for the problem
of designing revenue optimal Bayesian incentive compatible auctions when there
are multiple (heterogeneous) items and when bidders can have arbitrary demand
and budget constraints. Our mechanisms are surprisingly simple: We show that a
sequential all-pay mechanism is a 4 approximation to the revenue of the optimal
ex-interim truthful mechanism with discrete correlated type space for each
bidder. We also show that a sequential posted price mechanism is a O(1)
approximation to the revenue of the optimal ex-post truthful mechanism when the
type space of each bidder is a product distribution that satisfies the standard
hazard rate condition. We further show a logarithmic approximation when the
hazard rate condition is removed, and complete the picture by showing that
achieving a sub-logarithmic approximation, even for regular distributions and
one bidder, requires pricing bundles of items. Our results are based on
formulating novel LP relaxations for these problems, and developing generic
rounding schemes from first principles. We believe this approach will be useful
in other Bayesian mechanism design contexts.Comment: Final version accepted to STOC '10. Incorporates significant reviewer
comment
Optimal Auctions vs. Anonymous Pricing: Beyond Linear Utility
The revenue optimal mechanism for selling a single item to agents with
independent but non-identically distributed values is complex for agents with
linear utility (Myerson,1981) and has no closed-form characterization for
agents with non-linear utility (cf. Alaei et al., 2012). Nonetheless, for
linear utility agents satisfying a natural regularity property, Alaei et al.
(2018) showed that simply posting an anonymous price is an e-approximation. We
give a parameterization of the regularity property that extends to agents with
non-linear utility and show that the approximation bound of anonymous pricing
for regular agents approximately extends to agents that satisfy this
approximate regularity property. We apply this approximation framework to prove
that anonymous pricing is a constant approximation to the revenue optimal
single-item auction for agents with public-budget utility, private-budget
utility, and (a special case of) risk-averse utility.Comment: Appeared at EC 201
Sequential Posted Pricing and Multi-parameter Mechanism Design
We consider the classical mathematical economics problem of {\em Bayesian optimal mechanism design} where a principal aims to optimize expected revenue when allocating resources to self-interested agents with preferences drawn from a known distribution. In single-parameter settings (i.e., where each agent's preference is given by a single private value for being served and zero for not being served) this problem is solved [Myerson '81]. Unfortunately, these single parameter optimal mechanisms are impractical and rarely employed [Ausubel and Milgrom '06], and furthermore the underlying economic theory fails to generalize to the important, relevant, and unsolved multi-dimensional setting (i.e., where each agent's preference is given by multiple values for each of the multiple services available) [Manelli and Vincent '07]. In contrast to the theory of optimal mechanisms we develop a theory of sequential posted price mechanisms, where agents in sequence are offered take-it-or-leave-it prices. These mechanisms are approximately optimal in single-dimensional settings, and avoid many of the properties that make optimal mechanisms impractical. Furthermore, these mechanisms generalize naturally to give the first known approximations to the elusive optimal multi-dimensional mechanism design problem. In particular, we solve multi-dimensional multi-unit auction problems and generalizations to matroid feasibility constraints. The constant approximations we obtain range from 1.5 to 8. For all but one case, our posted price sequences can be computed in polynomial time.