The Info List - Predictability

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Predictability is the degree to which a correct prediction or forecast of a system's state can be made either qualitatively or quantitatively.


1 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 8 Techniques 9 In climate

9.1 The Spring Predictability Barrier

10 In macroeconomics 11 See also 12 References 13 External links

Predictability and Causality[edit] Causal determinism
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[edit] 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.[1] In statistical physics[edit] 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).[2] In mathematics[edit] 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.[3] 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.[4] In human–computer interaction[edit] 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,[5] Princeton University,[6] 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.[7] In human sentence processing[edit] Main article: 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 comprehension. In biology[edit] 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.[8] 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.[9] 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.[10] In popular culture[edit] 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.[11] Techniques[edit] 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 century.[12] 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 misfortunes.[13] In climate[edit] 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.[14] The Spring Predictability Barrier[edit] The Spring 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.[15] In macroeconomics[edit] 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.[16] See also[edit]

Randomness Contingency


^ 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. doi:10.1016/j.shpsa.2013.12.003.  ^ 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 2015-07-16. ^ 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 September 2017.  ^ Chen, Chenyi. "Deep Learning for Self -driving Car" (PDF). Princeton University. Retrieved 27 September 2017.  ^ http://news.mit.edu/2016/teaching-machines-to-predict-the-future-0621 ^ http://blogs.discovermagazine.com/notrocketscience/2011/04/11/justice-is-served-but-more-so-after-lunch-how-food-breaks-sway-the-decisions-of-judges/#.WcrXEq2ZP_Q ^ https://www.theguardian.com/science/2009/may/24/genes-human-attraction ^ https://link.springer.com/article/10.1007/BF00293209 ^ http://www.informationphilosopher.com/freedom/predictability.html ^ http://science.howstuffworks.com/science-vs-myth/extrasensory-perceptions/tarot-card6.htm ^ Guiley, Rosemary. "tasseomancy." The encyclopedia of witches, witchcraft, and wicca. 3rd ed. N.p.: Infobase Publishing, 2008. 341. Print. ^ " 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 2017.  ^ Diebold, Francis X. "Measuring Predictability: Theory and Macroeconomic Applications" (PDF). 

External links[edit]

Look up predictability in Wiktionary, the free dictionary.

v t e

Chaos theory

Chaos theory

Anosov diffeomorphism Bifurcation theory Butterfly effect Chaos theory
Chaos theory
in organizational development Complexity Control of chaos Dynamical system Edge of chaos Fractal Predictability Quantum chaos Santa Fe Institute Synchronization of chaos Unintended consequences

Chaotic maps (list)

Arnold tongue Arnold's cat map Baker's map Complex quadratic map Complex squaring map Coupled map lattice Double pendulum Double scroll attractor Duffing equation Duffing map Dyadic transformation Dynamical billiards


Exponential map Gauss map Gingerbreadman map Hénon map Horseshoe map Ikeda map Interval exchange map Kaplan–Yorke map Logistic map Lorenz system Multiscroll attractor Rabinovich–Fabrikant equations Rössler attractor Standard map Swinging Atwood's machine Tent map Tinkerbell map Van der Pol oscillator Zaslavskii map

Chaos systems

Bouncing ball dynamics Chua's circuit Economic bubble FPUT problem Tilt-A-Whirl

Chaos theorists

Michael Berry Mary Cartwright Leon O. Chua Mitchell Feigenbaum Celso Grebogi Martin Gutzwiller Brosl Hasslacher Michel Hénon Svetlana Jitomirskaya Sofia Kovalevskaya Bryna Kra Edward Norton Lorenz Aleksandr Lyapunov Benoît Mandelbrot Hee Oh Edward Ott Henri Poincaré Mary Rees Otto Rössler David Ruelle Caroline Series Oleksandr Mykolayovych Sharkovsky Nina Snaith Floris Takens Audrey Terras Mary Tsingou Amie Wilkinson James A. Yorke Lai-Sang Young

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Outline Index

Descriptive statistics

Continuous data



arithmetic geometric harmonic

Median Mode


Variance Standard deviation Coefficient of variation Percentile Range Interquartile range


Central limit theorem Moments

Skewness Kurtosis L-moments

Count data

Index of dispersion

Summary tables

Grouped data Frequency distribution Contingency table


Pearson product-moment correlation Rank correlation

Spearman's rho Kendall's tau

Partial correlation Scatter plot


Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q–Q plot Run chart Scatter plot Stem-and-leaf display Radar chart

Data collection

Study design

Population Statistic Effect size Statistical power Sample size determination Missing data

Survey methodology


stratified cluster

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Controlled experiments


control optimal

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Uncontrolled studies

Observational study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Population Statistic Probability distribution Sampling distribution

Order statistic

Empirical distribution

Density estimation

Statistical model

Lp space


location scale shape

Parametric family

Likelihood (monotone) Location–scale family Exponential family

Completeness Sufficiency Statistical functional

Bootstrap U V

Optimal decision

loss function

Efficiency Statistical distance


Asymptotics Robustness

Frequentist inference

Point estimation

Estimating equations

Maximum likelihood Method of moments M-estimator Minimum distance

Unbiased estimators

Mean-unbiased minimum-variance

Rao–Blackwellization Lehmann–Scheffé theorem



Interval estimation

Confidence interval Pivot Likelihood interval Prediction
interval Tolerance interval Resampling

Bootstrap Jackknife

Testing hypotheses

1- & 2-tails Power

Uniformly most powerful test

Permutation test

Randomization test

Multiple comparisons

Parametric tests

Likelihood-ratio Wald Score

Specific tests

Z-test (normal) Student's t-test F-test

Goodness of fit

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Cross validation AIC BIC

Rank statistics


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1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra)

Bayesian inference

Bayesian probability

prior posterior

Credible interval Bayes factor Bayesian estimator

Maximum posterior estimator

Correlation Regression analysis


Pearson product-moment Partial correlation Confounding
variable Coefficient of determination

Regression analysis

Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)

Linear regression

Simple linear regression Ordinary least squares General linear model Bayesian regression

Non-standard predictors

Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity

Generalized linear model

Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance

Analysis of variance
Analysis of variance
(ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis


Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test


Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model

Factor analysis

Multivariate distributions

Elliptical distributions




Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality

Specific tests

Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey

Time domain


partial (PACF)

(XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)

Frequency domain

Spectral density estimation Fourier analysis Wavelet Whittle likelihood


Survival function

Kaplan–Meier estimator
Kaplan–Meier estimator
(product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time

Hazard function

Nelson–Aalen estimator


Log-rank test



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