Predictability is the degree to which a correct prediction or forecast
of a system's state can be made either qualitatively or
Predictability and Causality
1.1 Laplace's Demon
2 In statistical physics
3 In mathematics
4 In human–computer interaction
5 In human sentence processing
6 In biology
7 In popular culture
9 In climate
9.1 The Spring
10 In macroeconomics
11 See also
13 External links
Predictability and Causality
Causal determinism has a strong relationship with predictability.
Perfect predictability implies strict determinism, but lack of
predictability does not necessarily imply lack of determinism.
Limitations on predictability could be caused by factors such as a
lack of information or excessive complexity.
In experimental physics, there are always observational errors
determining variables such as positions and velocities. So perfect
prediction is practically impossible. Moreover, in modern quantum
mechanics, Werner Heisenberg's indeterminacy principle puts limits on
the accuracy with which such quantities can be known. So such perfect
predictability is also theoretically impossible.
Laplace's Demon is a supreme intelligence who could completely predict
the one possible future given the Newtonian dynamical laws of
classical physics and perfect knowledge of the positions and
velocities of all the particles in the world. In other words, if it
were possible to have every piece of data on every atom in the
universe from the beginning of time, it would be possible to predict
the behavior of every atom into the future. Laplace’s determinism is
usually thought to be based on his mechanics, but he could not prove
mathematically that mechanics is deterministic. Rather, his
determinism is based on general philosophical principles, specifically
on the principle of sufficient reason and the law of continuity.
In statistical physics
Although the second law of thermodynamics can determine the
equilibrium state that a system will evolve to, and steady states in
dissipative systems can sometimes be predicted, there exists no
general rule to predict the time evolution of systems distanced from
equilibrium, e.g. chaotic systems, if they do not approach an
equilibrium state. Their predictability usually deteriorates with time
and to quantify predictability, the rate of divergence of system
trajectories in phase space can be measured (Kolmogorov–Sinai
entropy, Lyapunov exponents).
In stochastic analysis a random process is a predictable process if it
is possible to know the next state from the present time.
The branch of mathematics known as Chaos Theory focuses on the
behavior of systems that are highly sensitive to initial conditions.
It suggests that a small change in an initial condition can completely
alter the progression of a system. This phenomenon is known as the
butterfly effect, which claims that a butterfly flapping its wings in
Brazil can cause a tornado in Texas. The nature of chaos theory
suggests that the predictability of any system is limited because it
is impossible to know all of the minutiae of a system at the present
time. In principal, the deterministic systems that chaos theory
attempts to analyze can be predicted, but uncertainty in a forecast
increases exponentially with elapsed time.
In human–computer interaction
In the study of human–computer interaction, predictability is the
property to forecast the consequences of a user action given the
current state of the system.
A contemporary example of human-computer interaction manifests in the
development of computer vision algorithms for collision-avoidance
software in self-driving cars. Researchers at NVIDIA Corporation,
Princeton University, and other institutions are leveraging deep
learning to teach computers to anticipate subsequent road scenarios
based on visual information about current and previous states.
Another example of human-computer interaction are computer simulations
meant to predict human behavior based on algorithms. For example, MIT
has recently developed an incredibly accurate algorithm to predict the
behavior of humans. When tested against television shows, the
algorithm was able to predict with great accuracy the subsequent
actions of characters. Algorithms and computer simulations like these
show great promise for the future of artificial intelligence.
In human sentence processing
Prediction in language comprehension
Linguistic prediction is a phenomenon in psycholinguistics occurring
whenever information about a word or other linguistic unit is
activated before that unit is actually encountered. Evidence from
eyetracking, event-related potentials, and other experimental methods
indicates that in addition to integrating each subsequent word into
the context formed by previously encountered words, language users
may, under certain conditions, try to predict upcoming words.
Predictability has been shown to affect both text and speech
processing, as well as speech production. Further, predictability has
been shown to have an effect on syntactic, semantic and pragmatic
In the study of biology – particularly genetics and neuroscience –
predictability relates to the prediction of biological developments
and behaviors based on inherited genes and past experiences.
Significant debate exists in the scientific community over whether or
not a person's behavior is completely predictable based on their
genetics. Studies such as the one in Israel, which showed that judges
were more likely to give a lighter sentence if they had eaten more
recently. In addition to cases like this, it has been proven that
individuals smell better to someone with complementary immunity genes,
leading to more physical attraction.
Genetics can be examined to
determine if an individual is predisposed to any diseases, and
behavioral disorders can most often be explained by analyzing defects
in genetic code. Scientist who focus on examples like these argue that
human behavior is entirely predictable. Those on the other side of the
debate argue that genetics can only provide a predisposition to act a
certain way and that, ultimately, humans possess the free will to
choose whether or not to act.
Animals have significantly more predictable behavior than humans.
Driven by natural selection, animals develop mating calls, predator
warnings, and communicative dances. One example of these engrained
behaviors is the Belding's ground squirrel, which developed a specific
set of calls that warn nearby squirrels about predators. If a ground
squirrel sees a predator on land it will elicit a trill after it gets
to safety, which signals to nearby squirrels that they should stand up
on their hind legs and attempt to locate the predator. When a predator
is seen in the air, a ground squirrel will immediately call out a long
whistle, putting himself in danger but signaling for nearby squirrels
to run for cover. Through experimentation and examination scientists
have been able to chart behaviors like this and very accurately
predict how animals behave in certain situations.
In popular culture
The study of predictability often sparks debate between those who
believe humans maintain complete control over their free-will and
those who believe our actions are predetermined. However, it is likely
that neither Newton nor Laplace saw the study of predictability as
relating to determinism.
One example of prediction techniques is tarot cards. Tarot cards have
been used for hundreds of years to help in determining the future.
"Fortune tellers" have been traced through history back to the Ancient
Egyptians, however the earliest complete record dates only to the 18th
Tasseography is a divination method, typically utilizing tea leaves or
coffee grounds. Western tasseography first started in medieval Europe,
where fortune tellers would interpret splatters of wax, lead, and
various other molten substances. When the Dutch brought tea from China
in the seventeenth century, this evolved into tea tasseography.
Tasseography is typically performed in a bright colored cup to
contrast with the dark tea leaves or coffee grounds. The bright colors
symbolize good fortunes while the dark color symbolizes
As climate change and other weather phenomenon become more common, the
predictability of climate systems becomes more important. The IPCC
notes that our ability to predict future detailed climate interactions
is difficult, however, long term climate forecasts are possible.
Predictability Barrier refers to a period of time early in
the year when making summer weather predictions about the El
Niño–Southern Oscillation is difficult. It is unknown why it is
difficult, although many theories have been proposed. There is some
thought that the cause is due to the
ENSO transition where conditions
are more rapidly shifting.
Predictability in macroeconomics refers most frequently to the degree
to which an economic model accurately reflects quarterly data and the
degree to which one might successfully identify the internal
propagation mechanisms of models. Examples of US macroeconomic series
of interest include but are not limited to Consumption, Investment,
Real GNP, and Capital Stock. Factors that are involved in the
predictability of an economic system include the range of the forecast
(is the forecast two years "out" or twenty) and the variability of
estimates. Mathematical processes for assessing the predictability of
macroeconomic trends are still in development.
^ van Strien, Marij (2014-03-01). "On the origins and foundations of
Laplacian determinism". Studies in History and Philosophy of Science
Part A. 45 (Supplement C): 24–31.
^ Boeing, G. (2016). "Visual Analysis of Nonlinear Dynamical Systems:
Chaos, Fractals, Self-Similarity and the Limits of Prediction".
Systems. 4 (4): 37. doi:10.3390/systems4040037.
^ Boeing (2015). "Chaos Theory and the Logistic Map". Retrieved
^ Sync: The Emerging Science of Spontaneous Order, Steven Strogatz,
Hyperion, New York, 2003, pages 189-190.
^ "The AI Car Computer for Autonomous Driving". NVIDIA. Retrieved 27
^ Chen, Chenyi. "Deep Learning for Self -driving Car" (PDF). Princeton
University. Retrieved 27 September 2017.
^ Guiley, Rosemary. "tasseomancy." The encyclopedia of witches,
witchcraft, and wicca. 3rd ed. N.p.: Infobase Publishing, 2008. 341.
Predictability of the Climate System". Working Group I: The
Scientific Basis. IPCC. Retrieved 26 September 2017.
^ L'Heureux, Michelle. "The Spring
Predictability Barrier: we'd rather
be on Spring Break". Climate.gov. NOAA. Retrieved 26 September
^ Diebold, Francis X. "Measuring Predictability: Theory and
Macroeconomic Applications" (PDF).
Look up predictability in Wiktionary, the free dictionary.
Chaos theory in organizational development
Control of chaos
Edge of chaos
Santa Fe Institute
Synchronization of chaos
Arnold's cat map
Complex quadratic map
Complex squaring map
Coupled map lattice
Double scroll attractor
Interval exchange map
Swinging Atwood's machine
Van der Pol oscillator
Bouncing ball dynamics
Leon O. Chua
Edward Norton Lorenz
Oleksandr Mykolayovych Sharkovsky
James A. Yorke
Coefficient of variation
Central limit theorem
Index of dispersion
Pearson product-moment correlation
Sample size determination
Method of moments
1- & 2-tails
Uniformly most powerful test
Goodness of fit
Signed rank (Wilcoxon)
Rank sum (Mann–Whitney)
Ordered alternative (Jonckheere–Terpstra)
Maximum posterior estimator
Coefficient of determination
Errors and residuals
Regression model validation
Mixed effects models
Simultaneous equations models
Multivariate adaptive regression splines (MARS)
Simple linear regression
Ordinary least squares
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Partition of variance
Analysis of variance
Analysis of variance (ANOVA, anova)
Analysis of covariance
Degrees of freedom
Categorical / Multivariate / Time-series / Survival
Structural equation model
ARIMA model (Box–Jenkins)
Autoregressive conditional heteroskedasticity (ARCH)
Vector autoregression (VAR)
Spectral density estimation
Kaplan–Meier estimator (product limit)
Proportional hazards models
Accelerated failure time (AFT) model
First hitting time
Clinical trials / studies
Process / quality control
Geographic information system