114,050 research outputs found
Measuring the predictability of life outcomes with a scientific mass collaboration.
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences
A Non-Probabilistic Model of Relativised Predictability in Physics
Little effort has been devoted to studying generalised notions or models of
(un)predictability, yet is an important concept throughout physics and plays a
central role in quantum information theory, where key results rely on the
supposed inherent unpredictability of measurement outcomes. In this paper we
continue the programme started in [1] developing a general, non-probabilistic
model of (un)predictability in physics. We present a more refined model that is
capable of studying different degrees of "relativised" unpredictability. This
model is based on the ability for an agent, acting via uniform, effective
means, to predict correctly and reproducibly the outcome of an experiment using
finite information extracted from the environment. We use this model to study
further the degree of unpredictability certified by different quantum
phenomena, showing that quantum complementarity guarantees a form of
relativised unpredictability that is weaker than that guaranteed by
Kochen-Specker-type value indefiniteness. We exemplify further the difference
between certification by complementarity and value indefiniteness by showing
that, unlike value indefiniteness, complementarity is compatible with the
production of computable sequences of bits.Comment: 10 page
Limits to solar cycle predictability: Cross-equatorial flux plumes
Within the Babcock-Leighton framework for the solar dynamo, the strength of a
cycle is expected to depend on the strength of the dipole moment or net
hemispheric flux during the preceding minimum, which depends on how much flux
was present in each hemisphere at the start of the previous cycle and how much
net magnetic flux was transported across the equator during the cycle. Some of
this transport is associated with the random walk of magnetic flux tubes
subject to granular and supergranular buffeting, some of it is due to the
advection caused by systematic cross-equatorial flows such as those associated
with the inflows into active regions, and some crosses the equator during the
emergence process.
We aim to determine how much of the cross-equatorial transport is due to
small-scale disorganized motions (treated as diffusion) compared with other
processes such as emergence flux across the equator. We measure the
cross-equatorial flux transport using Kitt Peak synoptic magnetograms,
estimating both the total and diffusive fluxes. Occasionally a large sunspot
group, with a large tilt angle emerges crossing the equator, with flux from the
two polarities in opposite hemispheres. The largest of these events carry a
substantial amount of flux across the equator (compared to the magnetic flux
near the poles). We call such events cross-equatorial flux plumes. There are
very few such large events during a cycle, which introduces an uncertainty into
the determination of the amount of magnetic flux transported across the equator
in any particular cycle. As the amount of flux which crosses the equator
determines the amount of net flux in each hemisphere, it follows that the
cross-equatorial plumes introduce an uncertainty in the prediction of the net
flux in each hemisphere. This leads to an uncertainty in predictions of the
strength of the following cycle.Comment: A&A, accepte
Blind source separation using temporal predictability
A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals.
It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O (N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, sub-gaussian, and gaussian probability density functions and on mixtures of voices and music
Big data analyses reveal patterns and drivers of the movements of southern elephant seals
The growing number of large databases of animal tracking provides an
opportunity for analyses of movement patterns at the scales of populations and
even species. We used analytical approaches, developed to cope with big data,
that require no a priori assumptions about the behaviour of the target agents,
to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina)
in the Southern Ocean, that was comprised of >500,000 location estimates
collected over more than a decade. Our analyses showed that the displacements
of these seals were described by a truncated power law distribution across
several spatial and temporal scales, with a clear signature of directed
movement. This pattern was evident when analysing the aggregated tracks despite
a wide diversity of individual trajectories. We also identified marine
provinces that described the migratory and foraging habitats of these seals.
Our analysis provides evidence for the presence of intrinsic drivers of
movement, such as memory, that cannot be detected using common models of
movement behaviour. These results highlight the potential for big data
techniques to provide new insights into movement behaviour when applied to
large datasets of animal tracking.Comment: 18 pages, 5 figures, 6 supplementary figure
The limits to stock return predictability
We examine predictive return regressions from a new angle. We ask what observable
univariate properties of returns tell us about the “predictive space” that defines the true
predictive model: the triplet ¡
λ, R2
x, ρ¢
, where λ is the predictor’s persistence, R2
x is the
predictive R-squared, and ρ is the "Stambaugh Correlation" (between innovations in the
predictive system). When returns are nearly white noise, and the variance ratio slopes
downwards, the predictive space can be tightly constrained. Data on real annual US stock
returns suggest limited scope for even the best possible predictive regression to out-predict
the univariate representation, particularly over long horizons
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