831 research outputs found
Sequential item pricing for unlimited supply
We investigate the extent to which price updates can increase the revenue of
a seller with little prior information on demand. We study prior-free revenue
maximization for a seller with unlimited supply of n item types facing m myopic
buyers present for k < log n days. For the static (k = 1) case, Balcan et al.
[2] show that one random item price (the same on each item) yields revenue
within a \Theta(log m + log n) factor of optimum and this factor is tight. We
define the hereditary maximizers property of buyer valuations (satisfied by any
multi-unit or gross substitutes valuation) that is sufficient for a significant
improvement of the approximation factor in the dynamic (k > 1) setting. Our
main result is a non-increasing, randomized, schedule of k equal item prices
with expected revenue within a O((log m + log n) / k) factor of optimum for
private valuations with hereditary maximizers. This factor is almost tight: we
show that any pricing scheme over k days has a revenue approximation factor of
at least (log m + log n) / (3k). We obtain analogous matching lower and upper
bounds of \Theta((log n) / k) if all valuations have the same maximum. We
expect our upper bound technique to be of broader interest; for example, it can
significantly improve the result of Akhlaghpour et al. [1]. We also initiate
the study of revenue maximization given allocative externalities (i.e.
influences) between buyers with combinatorial valuations. We provide a rather
general model of positive influence of others' ownership of items on a buyer's
valuation. For affine, submodular externalities and valuations with hereditary
maximizers we present an influence-and-exploit (Hartline et al. [13]) marketing
strategy based on our algorithm for private valuations. This strategy preserves
our approximation factor, despite an affine increase (due to externalities) in
the optimum revenue.Comment: 18 pages, 1 figur
Randomized Revenue Monotone Mechanisms for Online Advertising
Online advertising is the main source of revenue for many Internet firms. A
central component of online advertising is the underlying mechanism that
selects and prices the winning ads for a given ad slot. In this paper we study
designing a mechanism for the Combinatorial Auction with Identical Items (CAII)
in which we are interested in selling identical items to a group of bidders
each demanding a certain number of items between and . CAII generalizes
important online advertising scenarios such as image-text and video-pod
auctions [GK14]. In image-text auction we want to fill an advertising slot on a
publisher's web page with either text-ads or a single image-ad and in
video-pod auction we want to fill an advertising break of seconds with
video-ads of possibly different durations.
Our goal is to design truthful mechanisms that satisfy Revenue Monotonicity
(RM). RM is a natural constraint which states that the revenue of a mechanism
should not decrease if the number of participants increases or if a participant
increases her bid.
[GK14] showed that no deterministic RM mechanism can attain PoRM of less than
for CAII, i.e., no deterministic mechanism can attain more than
fraction of the maximum social welfare. [GK14] also design a
mechanism with PoRM of for CAII.
In this paper, we seek to overcome the impossibility result of [GK14] for
deterministic mechanisms by using the power of randomization. We show that by
using randomization, one can attain a constant PoRM. In particular, we design a
randomized RM mechanism with PoRM of for CAII
Theory of collective opinion shifts: from smooth trends to abrupt swings
We unveil collective effects induced by imitation and social pressure by
analyzing data from three different sources: birth rates, sales of cell phones
and the drop of applause in concert halls. We interpret our results within the
framework of the Random Field Ising Model, which is a threshold model for
collective decisions accounting both for agent heterogeneity and social
imitation. Changes of opinion can occur either abruptly or continuously,
depending on the importance of herding effects. The main prediction of the
model is a scaling relation between the height h of the speed of variation peak
and its width of the form h ~ w^{-kappa}, with kappa = 2/3 for well
connected populations. Our three sets of data are compatible with such a
prediction, with kappa ~ 0.62 for birth rates, kappa ~ 0.71 for cell phones and
kappa ~ 0.64 for clapping. In this last case, we in fact observe that some
clapping samples end discontinuously (w=0), as predicted by the model for
strong enough imitation.Comment: 11 pages, 8 figure
Influence Diffusion in Social Networks under Time Window Constraints
We study a combinatorial model of the spread of influence in networks that
generalizes existing schemata recently proposed in the literature. In our
model, agents change behaviors/opinions on the basis of information collected
from their neighbors in a time interval of bounded size whereas agents are
assumed to have unbounded memory in previously studied scenarios. In our
mathematical framework, one is given a network , an integer value
for each node , and a time window size . The goal is to
determine a small set of nodes (target set) that influences the whole graph.
The spread of influence proceeds in rounds as follows: initially all nodes in
the target set are influenced; subsequently, in each round, any uninfluenced
node becomes influenced if the number of its neighbors that have been
influenced in the previous rounds is greater than or equal to .
We prove that the problem of finding a minimum cardinality target set that
influences the whole network is hard to approximate within a
polylogarithmic factor. On the positive side, we design exact polynomial time
algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared
in: Proceedings of 20th International Colloquium on Structural Information
and Communication Complexity (Sirocco 2013), Lectures Notes in Computer
Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201
Why is order flow so persistent?
Order flow in equity markets is remarkably persistent in the sense that order
signs (to buy or sell) are positively autocorrelated out to time lags of tens
of thousands of orders, corresponding to many days. Two possible explanations
are herding, corresponding to positive correlation in the behavior of different
investors, or order splitting, corresponding to positive autocorrelation in the
behavior of single investors. We investigate this using order flow data from
the London Stock Exchange for which we have membership identifiers. By
formulating models for herding and order splitting, as well as models for
brokerage choice, we are able to overcome the distortion introduced by
brokerage. On timescales of less than a few hours the persistence of order flow
is overwhelmingly due to splitting rather than herding. We also study the
properties of brokerage order flow and show that it is remarkably consistent
both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure
A novel technique for the treatment of post operative retro-rectal haematoma: two case reports
Rectal bleeding following any form of rectal surgery is a well recognised complication 1, 2, 3 & 4. However retro-rectal bleeding and tracking which then presents as rectal bleeding has not been reported in the literature. We describe a novel way of dealing with this technically difficult post-operative complication
Information and ambiguity: herd and contrarian behaviour in financial markets
“The final publication is available at Springer via http://dx.doi.org/10.1007/s11238-012-9334-3”The paper studies the impact of informational ambiguity on behalf of informed traders
on history-dependent price behaviour in a model of sequential trading in nancial markets.
Following Chateauneuf, Eichberger and Grant (2006), we use neo-additive capacities to
model ambiguity. Such ambiguity and attitudes to it can engender herd and contrarian
behaviour, and also cause the market to break down. The latter, herd and contrarian
behaviour, can be reduced by the existence of a bid-ask spread.Research in part funded by ESRC grant RES-000-22-0650
An Experimental Study of Cryptocurrency Market Dynamics
As cryptocurrencies gain popularity and credibility, marketplaces for
cryptocurrencies are growing in importance. Understanding the dynamics of these
markets can help to assess how viable the cryptocurrnency ecosystem is and how
design choices affect market behavior. One existential threat to
cryptocurrencies is dramatic fluctuations in traders' willingness to buy or
sell. Using a novel experimental methodology, we conducted an online experiment
to study how susceptible traders in these markets are to peer influence from
trading behavior. We created bots that executed over one hundred thousand
trades costing less than a penny each in 217 cryptocurrencies over the course
of six months. We find that individual "buy" actions led to short-term
increases in subsequent buy-side activity hundreds of times the size of our
interventions. From a design perspective, we note that the design choices of
the exchange we study may have promoted this and other peer influence effects,
which highlights the potential social and economic impact of HCI in the design
of digital institutions.Comment: CHI 201
Overthrowing the dictator: a game-theoretic approach to revolutions and media
A distinctive feature of recent revolutions was the key role of social media (e.g. Facebook, Twitter and YouTube). In this paper, we study its role in mobilization. We assume that social media allow potential participants to observe the individual participation decisions of others, while traditional mass media allow potential participants to see only the total number of people who participated before them. We show that when individuals’ willingness to revolt is publicly known, then both sorts of media foster a successful revolution. However, when willingness to revolt is private information, only social media ensure that a revolt succeeds, with mass media multiple outcomes are possible, one of which has individuals not participating in the revolt. This suggests that social media enhance the likelihood that a revolution triumphs more than traditional mass media
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