- Applications of randomness
Randomnesshas many uses in gambling, divination, statistics, cryptography, art, etc.
Note that these uses may have different requirements when it comes to
statistical randomnessor unpredictability, which in turn leads to different randomizationmethods. For example, applications in cryptography have strict requirements, whereas other uses (such as generating a "quote of the day") don't need more than "shallow" randomness.
Unpredictable random numbers were first investigated in the context of
gambling, and many randomizing devices such as dice, shuffling playing cards, and roulettewheels, were first developed for use in games of chance. Fairly produced random numbers are vital to electronic gambling and ways of creating them are sometimes regulated by governmental gaming commissions.
casinogames contain often one or more random number generators that decide the outcome of a trial in the game. Even in slot machines, where mechanical reels appear to spin randomly, the reels are actually spinning for entertainment value and eventually stop exactly where the machine's software decided they would stop when the handle was first pulled. (It has been alleged that some gaming machines' software is deliberately biased to prevent true randomness, in the interests of maximizing their owners' revenue -- this sort of allegation is why state gaming inspectors exist.)
Random draws are often used to make a decision where no rational or fair basis exists for making a deterministic decision, or to make unpredictable moves.
Many ancient cultures saw random events in nature as signs from the
gods. Still today, much of divinationis based on the observation of random processes.
Random numbers are often used in
parapsychologyas a test of precognition.
Fifth Century BC
Athenian Democracydeveloped out of a notion of isonomia(equality of political rights), and random selection was the principal way of achieving this fairness [Herodotus 3.80] . Greek " Democracy" (literally meaning rule by the people) was literally run by the people: the administration was in the hands of committees allotted from the people. Although it may seem strange to those used to Liberal Democracythe Athenian Greeks considered electionsto be undemocraticThe Athenian Democracy in the Age of Demosthenes", Mogens Herman Hansen, ISBN 1-85399-585-1] [“it is thought to be democratic for the offices to be assigned by lot, for them to be elected is oligarchic,” [Aristotle, Politics 4.1294b] ] . This was because citizens chosen on merit or popularity contradicted the democratic principle of equality of all citizenry. In addition, allotment prevented the corrupt practise of buying votes as no one could know who would be selected as a magistrate or to sit on the Jury.
Allotment is today restricted mainly to the selection of
jurors in Anglo-Saxon legal systems like the UKand US. Proposals have been made for its use in government such as a new constitution for Iraq [http://www.sortition.org.uk] and various proposals for Upper Houses chosen by allotment(see Lords Reform)
Random numbers have uses in
physics(such as noise resonancestudies), engineering, and operations research. Many methods of statistical analysis, such as the bootstrap method, require random numbers. Monte Carlo methods in physics and computer science require random numbers to function.
Statistical practiceis based on statistical theorywhich, itself is founded on the concept of randomness. Many elements of statistical practice depend on the emulation of randomness through random numbers. Where those random numbers fall short of the conceptual ideal of randomness any subsequent statistical analysis may suffer from systematic bias. Elements of statistical practice that depend on randomness include: choosing a representative sample, disguising the protocol of a study from a participant (see randomized controlled trial) and Monte Carlo simulation.
These applications are useful in
auditing(for determining samples - such as invoices) and experimental design(for example in the creation of double-blind trials).
Many experiments in physics rely on a statistical analysis of their output. For example, an experiment might collect
X-rays from an astronomical source and then analyze the result for periodic signals. Since random noise can be expected to appear to have very faint periodic signals embedded in it, statistical analysis is required to determine the likelihood that a given signal actually represents a genuine signal. Testing such analysis methods requires the generation of random numbers. If the statistical method is extremely sensitive to patterns in the data (such as those used to search for binary pulsars) then very large amounts of data with no recognizable pattern are needed.
In many scientific and engineering fields, computer simulations of real phenomena are essential to understanding. When the real phenomena are affected by unpredictable processes, such as radio noise or day-to-day weather, these processes must be simulated using random or pseudo-random numbers.
Random number generators were originally constructed to carry out computer simulation of physical phenomena, specifically the simulation of neutron transportin nuclear fission.
Pseudo-random numbers are frequently used in
simulationof statistical events, a very simple example being the outcome of tossing a coin. More complicated implications are the simulation of genetics in whole populations, or the behaviour of sub-atomic particles. Such simulation methods, often called stochastic simulation methods have many applications in computer simulation of real-world effects.
Some not very rational projects such as the
Global Consciousness Projectexist which monitors fluctuations in the randomness of the numbers generated by a large number of hardware random number generators in an attempt to predict the scope of an event in near future. The intent is to prove that large scale events that are about to happen build up a sort of pressure (possibly quantum) that affects the RNGs.
A ubiquitous use of unpredictable random numbers is in
cryptographywhich underlies most of the attempts to provide security in modern communications (e.g., confidentiality, authentication, electronic commerce, etc.).
For example, if a user wants to use an
encryption algorithm, it is best that they select a random number as the key. These numbers must be completely unguessable to anyone else. The only way to practically manufacture such numbers is to use random numbers. If this is not done properly, security can be compromised. For example, if a simple 32 bit linear congruential pseudo-random number generator of the type supplied with most programming languages is used, then there will only be some four billion possible keys that can be produced before the generator repeats itself. A suitably motivated adversary could simply test them all. Even if a more sophisticated random number generator is used, its seed might be guessed (perhaps it is the time of day when the key was generated), and then keys can be predicted. (A vulnerability of this sort was famously discovered in an early release of Netscape Navigator, forcing the authors to quickly find a source of "more random" random numbers). Thus for this application, some truly random numbers are required.
Truly random numbers are absolutely required to be assured of the theoretical security provided by the
one-time pad— the only provably unbreakable encryptionalgorithm. Furthermore, those random sequences cannot be reused and must never become available to any attacker, which implies a continuously operable generator. See Venonafor an example of what happens when these requirements are violated when using a one-time pad.
For cryptographic purposes, one normally assumes some upper limit on the work an adversary can do (usually this limit is astronomically sized). If one has a pseudo-random number generator whose output is "sufficiently difficult" to predict for an unknown seed (such as a
stream cipher), one can generate true random numbers to fill the seed and then use the pseudo-random numbers in cryptographic applications. Such random number generators are called cryptographically secure pseudo-random number generators, and several have been implemented (for example, the /dev/urandom device available on most Unixes, the Yarrow server, and AT&T Bell Labs "truerand"). As with all cryptographic software, there are subtle issues beyond those discussed here, so care is certainly indicated in actual practice. In any case, it is often impossible to avoid the need for true (i.e., hardware) random number generators.
Since a requirement in cryptography is unpredictability to an attacker, "any" published random sequence is a poor choice, as are such sequences as the digits in an
irrational numbersuch as the φ or even in transcendental numberssuch as π, or e. Put another way, in cryptography, random bit streams need to be not only random, but also secret and hence unpredictable. Public or third-party sources of random values, or random values computed from publicly observable phenomena (weather, sports game results, stock prices), are almost never cryptographically acceptable, though often tempting and too often used by the unwary. They permit attacks that should never be allowed.
Since most cryptographic applications require a few thousand
bits at most, slow random number generators serve well—if they are actually random. This use of random generators is important; many informed observers believe every computer should have a way to generate true random numbers.
Literature, music and art
A clear example of randomness occurs in the problem of arranging items in an art exhibit. Usually this is avoided by using a theme. As John Cage pointed out, "While there are many ways that sounds might be produced [i.e., in terms of patterns] , few are attempted". Similarly, the arrangement of art in exhibits is often deliberately non-random. One case of this was Hitler's attempt to portray modern art in the worst possible light by arranging works in worst possible manner.Fact|date=June 2007 A case can be made for trying to make art in the worst possible way; i.e., either as
anti-art, or as actually random art. Dadaismas well as many other movements in art and letters have attempted to deal with randomness in various forms. Often people mistake order for randomness based on lack of information; e.g., Jackson Pollock's drippaintings, Helen Frankenthaler's abstractions (e.g., "For E.M."). Thus, in theory of art, all art is random in that it's "just paint and canvas" (the explanation of Frank Stella's work).
Similarly, the "unexpected" ending is part of the nature of interesting literature. A chief example of this is
Denis Diderot's novel "Jacques le fataliste" (literally: "James the Fatalist"; sometimes referred to as "Jacques the Fatalist" or "Jacques the Servant and his Master"). At one point in the novel, Diderot speaks directly to the reader:
Now I, as the author of this novel might have them set upon by thieves, or I might have them rest by a tree until the rain stops, but in fact they kept on walking and then near night-fall they could see the light of an inn in the distance.(not an exact quote). Diderot was making the point that the novel (a new invention then) was in fact random (in the sense of being invented out of thin air by the author). See also
Eugenio Montale, Theatre of the Absurd.
musicis deemed postmodern, including John Cage's chance derived " Music of Changes", Iannis Xenakis' stochasticmusic, aleatoric music, indeterminate music, or generative music.
Random numbers are also used in situations where "fairness" is approximated by
randomization, such as selecting jurors and military draft lotteries. In the Book of Numbers(33:54), Mosescommands the Israelites to apportion the land by lot (גורל).
Other examples might be generating a "Random Quote of the Day" for a website, or determining which way a villain might move in a
Randomness is also used in fanfiction, to describe events in stories that happen without much importance to plot, usually on purpose for humoruous reasons.
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