630 research outputs found
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
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
Envy Freedom and Prior-free Mechanism Design
We consider the provision of an abstract service to single-dimensional
agents. Our model includes position auctions, single-minded combinatorial
auctions, and constrained matching markets. When the agents' values are drawn
from a distribution, the Bayesian optimal mechanism is given by Myerson (1981)
as a virtual-surplus optimizer. We develop a framework for prior-free mechanism
design and analysis. A good mechanism in our framework approximates the optimal
mechanism for the distribution if there is a distribution; moreover, when there
is no distribution this mechanism still performs well.
We define and characterize optimal envy-free outcomes in symmetric
single-dimensional environments. Our characterization mirrors Myerson's theory.
Furthermore, unlike in mechanism design where there is no point-wise optimal
mechanism, there is always a point-wise optimal envy-free outcome.
Envy-free outcomes and incentive-compatible mechanisms are similar in
structure and performance. We therefore use the optimal envy-free revenue as a
benchmark for measuring the performance of a prior-free mechanism. A good
mechanism is one that approximates the envy free benchmark on any profile of
agent values. We show that good mechanisms exist, and in particular, a natural
generalization of the random sampling auction of Goldberg et al. (2001) is a
constant approximation
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