Christina Aperjis, Bernardo A. Huberman, Fang Wu
posted by Matúš Medo
(1 September 2010)
When foraging for information, users face a tradeoff between the accuracy and
value of the acquired information and the time spent collecting it, a problem
which also surfaces when seeking answers to a question posed to a large
community. We empirically study how people behave when facing these conflicting
objectives using data from Yahoo Answers, a community driven
question-and-answer site. We first study how users behave when trying to
maximize the amount of acquired information while minimizing the waiting time.
We find that users are willing to wait longer for an additional answer if they
have received a small number of answers. We then assume that users make a
sequence of decisions, deciding to wait for an additional answer as long as the
quality of the current answer exceeds some threshold. The resulting probability
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stribution for the number of answers that a question gets is an inverse
Gaussian, a fact that is validated by our data.
Masaaki Fukasawa
posted by Matúš Medo
(1 September 2010)
We study specific nonlinear transformations of the Black-Scholes implied
volatility to show remarkable properties of the volatility surface. Model-free
bounds on the implied volatility skew are given. Pricing formulas for the
European options which are written in terms of the implied volatility are
given. In particular, we prove elegant formulas for the fair strikes of the
variance swap and the gamma swap.
David W. Hogg, Jo Bovy, Dustin Lang
posted by Matúš Medo
(30 August 2010)
We go through the many considerations involved in fitting a model to data,
using as an example the fit of a straight line to a set of points in a
two-dimensional plane. Standard weighted least-squares fitting is only
appropriate when there is a dimension along which the data points have
negligible uncertainties, and another along which all the uncertainties can be
described by Gaussians of known variance; these conditions are rarely met in
practice. We consider cases of general, heterogeneous, and arbitrarily
covariant two-dimensional uncertainties, and situations in which there are bad
data (large outliers), unknown uncertainties, and unknown but expected
intrinsic scatter in the linear relationship being fit. Above all we emphasize
the importance of having a "generative model" for the data, even an approximate
one. Once there is a generative model, the subsequent fitting is non-arbitrary
because the model permits direct computation of the likelihood of the
parameters or the posterior probability distribution. Construction of a
pos
53d
terior probability distribution is indispensible if there are "nuisance
parameters" to marginalize away.
Riccardo Boero, Giangiacomo Bravo, Flaminio Squazzoni
posted by Matúš Medo
(30 August 2010)
This paper presents an experimentally grounded model on the relevance of
partner selection for the emergence of trust and cooperation among individuals.
By combining experimental evidence and network simulation, our model
investigates the link of interaction outcome and social structure formation and
shows that dynamic networks lead to positive outcomes when cooperators have the
capability of creating more links and isolating free-riders. By emphasizing the
self-reinforcing dynamics of interaction outcome and structure formation, our
results cast the argument about the relevance of interaction continuity for
cooperation in new light and provide insights to guide the design of new lab
experiments.
Stefan Thurner, Rudolf Hanel
posted by Matúš Medo
(27 August 2010)
One of the virtues of peer review is that it provides a self-regulating
selection mechanism for scientific work, papers and projects. Peer review as a
selection mechanism is hard to evaluate in terms of its efficiency. Serious
efforts to understand its strengths and weaknesses have not yet lead to clear
answers. In theory peer review works if the involved parties (editors and
referees) conform to a set of requirements, such as love for high quality
science, objectiveness, and absence of biases, nepotism, friend and clique
networks, selfishness, etc. If these requirements are violated, what is the
effect on the selection of high quality work? We study this question with a
simple agent based model. In particular we are interested in the effects of
rational referees, who might not have any incentive to see high quality work
other than their own published or promoted. We find that a small fraction of
incorrect (selfish or rational) referees can drastically reduce the quality of
the published (accepted) scientific standard. We quantify the fraction for
which peer review will no longer select better than pure chance. Decline of
quality of accepted scientific work is shown as a function of the fraction of
rational and unquali
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fied referees. We show how a simple quality-increasing
policy of e.g. a journal can lead to a loss in overall scientific quality, and
how mutual support-networks of authors and referees deteriorate the system.