17,989 research outputs found
Approximately Optimal Mechanism Design: Motivation, Examples, and Lessons Learned
Optimal mechanism design enjoys a beautiful and well-developed theory, and
also a number of killer applications. Rules of thumb produced by the field
influence everything from how governments sell wireless spectrum licenses to
how the major search engines auction off online advertising. There are,
however, some basic problems for which the traditional optimal mechanism design
approach is ill-suited --- either because it makes overly strong assumptions,
or because it advocates overly complex designs. The thesis of this paper is
that approximately optimal mechanisms allow us to reason about fundamental
questions that seem out of reach of the traditional theory.
This survey has three main parts. The first part describes the approximately
optimal mechanism design paradigm --- how it works, and what we aim to learn by
applying it. The second and third parts of the survey cover two case studies,
where we instantiate the general design paradigm to investigate two basic
questions. In the first example, we consider revenue maximization in a
single-item auction with heterogeneous bidders. Our goal is to understand if
complexity --- in the sense of detailed distributional knowledge --- is an
essential feature of good auctions for this problem, or alternatively if there
are simpler auctions that are near-optimal. The second example considers
welfare maximization with multiple items. Our goal here is similar in spirit:
when is complexity --- in the form of high-dimensional bid spaces --- an
essential feature of every auction that guarantees reasonable welfare? Are
there interesting cases where low-dimensional bid spaces suffice?Comment: Based on a talk given by the author at the 15th ACM Conference on
Economics and Computation (EC), June 201
A Novel Method to Calculate Click Through Rate for Sponsored Search
Sponsored search adopts generalized second price (GSP) auction mechanism
which works on the concept of pay per click which is most commonly used for the
allocation of slots in the searched page. Two main aspects associated with GSP
are the bidding amount and the click through rate (CTR). The CTR learning
algorithms currently being used works on the basic principle of (#clicks_i/
#impressions_i) under a fixed window of clicks or impressions or time. CTR are
prone to fraudulent clicks, resulting in sudden increase of CTR. The current
algorithms are unable to find the solutions to stop this, although with the use
of machine learning algorithms it can be detected that fraudulent clicks are
being generated. In our paper, we have used the concept of relative ranking
which works on the basic principle of (#clicks_i /#clicks_t). In this
algorithm, both the numerator and the denominator are linked. As #clicks_t is
higher than previous algorithms and is linked to the #clicks_i, the small
change in the clicks which occurs in the normal scenario have a very small
change in the result but in case of fraudulent clicks the number of clicks
increases or decreases rapidly which will add up with the normal clicks to
increase the denominator, thereby decreasing the CTR.Comment: 10 pages, 1 figur
Characterizing Optimal Adword Auctions
We present a number of models for the adword auctions used for pricing
advertising slots on search engines such as Google, Yahoo! etc. We begin with a
general problem formulation which allows the privately known valuation per
click to be a function of both the identity of the advertiser and the slot. We
present a compact characterization of the set of all deterministic incentive
compatible direct mechanisms for this model. This new characterization allows
us to conclude that there are incentive compatible mechanisms for this auction
with a multi-dimensional type-space that are {\em not} affine maximizers. Next,
we discuss two interesting special cases: slot independent valuation and slot
independent valuation up to a privately known slot and zero thereafter. For
both of these special cases, we characterize revenue maximizing and efficiency
maximizing mechanisms and show that these mechanisms can be computed with a
worst case computational complexity and respectively,
where is number of bidders and is number of slots. Next, we
characterize optimal rank based allocation rules and propose a new mechanism
that we call the customized rank based allocation. We report the results of a
numerical study that compare the revenue and efficiency of the proposed
mechanisms. The numerical results suggest that customized rank-based allocation
rule is significantly superior to the rank-based allocation rules.Comment: 29 pages, work was presented at a) Second Workshop on Sponsored
Search Auctions, Ann Arbor, MI b) INFORMS Annual Meeting, Pittsburgh c)
Decision Sciences Seminar, Fuqua School of Business, Duke Universit
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