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'{{pp-move-indef|small=yes}} {{redirect|Random}} {{selfref|For a random Wikipedia article, see [[Special:Random]]. For information about Wikipedia's random article feature, see [[Wikipedia:Random]].}} {{multiple issues |cleanup=March 2010 |refimprove=March 2010}} '''Randomness''' has somewhat disparate meanings as used in several different fields. It also has common meanings which may have loose connections with some of those more definite meanings. The [[Oxford English Dictionary]] defines "random" thus: <blockquote> Having no definite aim or purpose; not sent or guided in a particular direction; made, done, occurring, etc., without method or conscious choice; haphazard. </blockquote> Closely connected, therefore, with the concepts of chance, [[probability]], and [[information entropy]], randomness implies a lack of [[predictability]]. Randomness is a concept of non-[[:wikt:order|order]] or non-[[:wikt:coherence|coherence]] in a sequence of [[symbol]]s or [[step]]s, such that there is no intelligible pattern or combination. The fields of mathematics, probability, and statistics use formal definitions of randomness. In mathematics, a [[random variable]] is a way to assign a value to each possible outcome of an event. In probability and statistics, a [[random process]] is a repeating process whose outcomes follow no describable [[determinism|deterministic]] pattern, but follow a [[probability distribution]], such that the relative probability of the occurrence of each outcome can be approximated or calculated. For example, the rolling of a fair six-sided die in neutral conditions may be said to produce random results, because one cannot know, before a roll, what number will show up. However, the probability of rolling any one of the six rollable numbers can be calculated. The term is often used in [[statistics]] to signify well-defined statistical properties, such as a lack of [[bias (statistics)|bias]] or [[correlation]]. [[Monte Carlo Method]]s, which rely on random input, are important techniques in science, as, for instance, in [[Scientific computing|computational science]].<ref>[http://www.people.fas.harvard.edu/~junliu/Workshops/workshop2007/ Third Workshop on Monte Carlo Methods], Jun Liu, Professor of Statistics, Harvard University</ref> Random selection is an official method to resolve [[Tie (draw)|tied]] elections in some jurisdictions<ref>Municipal Elections Act (Ontario, Canada) 1996, c. 32, Sched., s. 62 (3) : "If the recount indicates that two or more candidates who cannot both or all be declared elected to an office have received the same number of votes, the clerk shall choose the successful candidate or candidates by lot."</ref> and is even an ancient method of [[divination]], as in [[tarot]], the [[I Ching]], and [[bibliomancy]]. Its use in politics is very old, as office holders in Ancient Athens were chosen by lot, there being no voting. == History == {{Main|History of randomness}} [[File:Pompeii - Osteria della Via di Mercurio - Dice Players.jpg|thumb|Ancient [[fresco]] of dice players in [[Pompei]].]] In ancient history, the concepts of chance and randomness were intertwined with that of fate. Many ancient peoples threw dice to determine fate, and this later evolved into games of chance. Most ancient cultures used various methods of [[divination]] to attempt to circumvent randomness and fate.<ref>''Handbook to life in ancient Rome'' by Lesley Adkins 1998 ISBN 0195123328 page 279</ref><ref>''Religions of the ancient world'' by Sarah Iles Johnston 2004 ISBN 0674015177 page 370</ref> The Chinese were perhaps the earliest people to formalize odds and chance 3,000 years ago. The Greek philosophers discussed randomness at length, but only in non-quantitative forms. It was only in the sixteenth century that Italian mathematicians began to formalize the odds associated with various games of chance. The invention of the [[calculus]] had a positive impact on the formal study of randomness. In the 1888 edition of his book ''The Logic of Chance'' [[John Venn]] wrote a chapter on "The conception of randomness" which included his view of the randomness of the digits of the number [[Pi]] by using them to construct a [[random walk]] in two dimensions.<ref>''Annotated readings in the history of statistics'' by Herbert Aron David, 2001 ISBN 0387988440 page 115. Note that the 1866 edition of Venn's book (on Google books) does not include this chapter.</ref> The early part of the twentieth century saw a rapid growth in the formal analysis of randomness, as various approaches to the mathematical foundations of probability were introduced. In the mid- to late-twentieth century, ideas of [[algorithmic information theory]] introduced new dimensions to the field via the concept of [[algorithmic randomness]]. Although randomness had often been viewed as an obstacle and a nuisance for many centuries, in the twentieth century computer scientists began to realize that the ''deliberate'' introduction of randomness into computations can be an effective tool for designing better algorithms. In some cases such [[randomized algorithms]] outperform the best deterministic methods. == Randomness in science == Many scientific fields are concerned with randomness: * [[Algorithmic probability]] * [[Chaos theory]] * [[Cryptography]] * [[Game theory]] * [[Information theory]] * [[Pattern recognition]] * [[Probability theory]] * [[Quantum mechanics]] * [[Statistics]] * [[Statistical mechanics]] === In the physical sciences === In the 19th century, scientists used the idea of random motions of molecules in the development of [[statistical mechanics]] in order to explain phenomena in [[thermodynamics]] and [[gas laws|the properties of gases]]. According to several standard interpretations of [[quantum mechanics]], microscopic phenomena are objectively random.<ref>[http://www.nature.com/nature/journal/v446/n7138/abs/nature05677.html] in [[Bell's aspect experiment]]: [[Journal: Nature]]</ref> That is, in an experiment where all causally relevant parameters are controlled, there will still be some aspects of the outcome which vary randomly. An example of such an experiment is placing a single unstable [[atom]] in a controlled environment; it cannot be predicted how long it will take for the atom to decay; only the probability of decay within a given time can be calculated.<ref>"Each nucleus decays spontaneously, at random, in accordance with the blind workings of chance". ''Q for Quantum'', [[John Gribbin]]</ref> Thus, quantum mechanics does not specify the outcome of individual experiments but only the probabilities. [[hidden variable theory|Hidden variable theories]] are inconsistent with the view that nature contains irreducible randomness: such theories posit that in the processes that appear random, properties with a certain statistical distribution are somehow at work "behind the scenes" determining the outcome in each case. === In biology === The [[modern evolutionary synthesis]] ascribes the observed diversity of life to [[natural selection]], in which some random genetic [[mutation]]s are retained in the [[gene pool]] due to the ''non-random'' improved chance for survival and reproduction that those mutated genes confer on individuals who possess them. The characteristics of an organism arise to some extent deterministically (e.g., under the influence of genes and the environment) and to some extent randomly. For example, the ''density'' of [[freckles]] that appear on a person's skin is controlled by genes and exposure to light; whereas the exact location of ''individual'' freckles seems to be random.<ref>{{cite journal |last= Breathnach |first= A. S. |year= 1982 |title= A long-term hypopigmentary effect of thorium-X on freckled skin |journal= British Journal of Dermatology |volume= 106 |issue= 1 |pages= 19–25 |doi= 10.1111/j.1365-2133.1982.tb00897.x |quote= The distribution of freckles seems to be entirely random, and not associated with any other obviously punctuate anatomical or physiological feature of skin. |pmid= 7059501}}</ref> Randomness is important if an animal is to behave in a way that is unpredictable to others. For instance, insects in flight tend to move about with random changes in direction, making it difficult for pursuing predators to predict their trajectories. === In mathematics === The mathematical theory of [[probability]] arose from attempts to formulate mathematical descriptions of chance events, originally in the context of [[gambling]], but later in connection with physics. [[Statistics]] is used to infer the underlying [[probability distribution]] of a collection of empirical observations. For the purposes of [[simulation]], it is necessary to have a large supply of [[Random sequence|random numbers]] or means to generate them on demand. [[Algorithmic information theory]] studies, among other topics, what constitutes a [[random sequence]]. The central idea is that a string of [[bit]]s is random if and only if it is shorter than any computer program that can produce that string ([[Kolmogorov randomness]])—this means that random strings are those that cannot be [[data compression|compressed]]. Pioneers of this field include [[Andrey Kolmogorov]] and his student [[Per Martin-Löf]], [[Ray Solomonoff]], and [[Gregory Chaitin]]. In mathematics, there must be an infinite expansion of information for randomness to exist. This can best be seen with an example. Given a random sequence of three-bit numbers, each number can have one of only eight possible values: 000, 001, 010, 011, 100, 101, 110, 111 Therefore, as the random sequence progresses, it must recycle the values it previously used. In order to increase the information space, another bit may be added to each possible number, giving 16 possible values from which to pick a random number. It could be said that the random four-bit number sequence is more random than the three-bit one. This suggests that in order to have true randomness, there must be an infinite expansion of the information space. Randomness is said to occur in numbers such as [[binary logarithm|log (2)]] and [[Pi]]. The decimal digits of Pi constitute an infinite sequence and "never repeat in a cyclical fashion". Numbers like pi are also thought to be [[normal number|normal]], which means that their digits are random in a certain statistical sense. <blockquote> Pi certainly seems to behave this way. In the first six billion decimal places of pi, each of the digits from 0 through 9 shows up about six hundred million times. Yet such results, conceivably accidental, do not prove normality even in base 10, much less normality in other number bases.<ref>[http://www.lbl.gov/Science-Articles/Archive/pi-random.html Are the digits of pi random? researcher may hold the key.]</ref></blockquote> === In statistics === In statistics, randomness is commonly used to create [[simple random samples]]. This allows surveys to be done with completely random groups of people to allow realistic data. Common methods of doing this are "drawing names out of a hat" or using a random digit chart. A random digit chart is simply a large table of random digits. === In information science === In information science, irrelevant or meaningless data is considered to be noise. Noise consists of a large number of transient disturbances with a statistically randomized time distribution. In [[communication theory]], randomness in a signal is called "noise" and is opposed to that component of its variation that is causally attributable to the source, the signal. In terms of the development of random networks, for communication randomness rests on the two simple assumptions of [[Paul Erdős]] and [[Alfréd Rényi]] who said that there were a fixed number of nodes and this number remained fixed for the life of the network, and that all nodes were equal and linked randomly to each other.{{Clarify|date=March 2011}}<ref>Laszso Barabasi, (2003), Linked, Rich Gets Richer, P81</ref> === In finance === The [[random walk hypothesis]] considers that asset prices in an organized [[market]] evolve at random. Other so-called random factors intervene in trends and patterns to do with supply-and-demand distributions. As well as this, the random factor of the environment itself results in fluctuations in stock and broker markets. === Randomness versus unpredictability === Randomness, as opposed to unpredictability, is held to be an objective property - [[determinist]]s believe it is an ''objective'' fact that randomness does not in fact exist. Also, what ''appears'' random to one observer may not appear random to another. Consider two observers of a sequence of bits, when only one of whom has the cryptographic key needed to turn the sequence of bits into a readable message. For that observer the message is not random, but it is unpredictable for the other. One of the intriguing aspects of random processes is that it is hard to know whether a process is truly random. An observer may suspect that there is some "key" that unlocks the message. This is one of the foundations of [[superstition]], but also a motivation for discovery in [[science]] and [[mathematics]]. Under the cosmological hypothesis of [[determinism]], there is no randomness in the universe, only [[predictability|unpredictability]], since there is only one possible outcome to all events in the universe. A follower of the narrow [[frequentist statistics|frequency interpretation of probability]] could assert that no event can be said to have [[probability]], since there is only one universal outcome. On the other hand, under the rival [[Bayesian probability|Bayesian interpretation of probability]] there is no objection to the use of probabilities in order to represent a lack of complete knowledge of the outcomes. Some mathematically defined sequences, such as the decimals of [[pi]] mentioned above, exhibit some of the same characteristics as random sequences, but because they are generated by a describable mechanism, they are called ''[[pseudorandom]]''. To an observer who does not know the mechanism, a pseudorandom sequence is unpredictable. [[chaos theory|Chaotic systems]] are unpredictable in practice due to their extreme sensitivity to initial conditions. Whether or not they are unpredictable in terms of [[computability theory (computation)|computability theory]] is a subject of current research. At least in some disciplines of computability theory, the notion of randomness is identified with computational unpredictability. Individual events that are random may still be precisely described ''en masse'', usually in terms of probability or expected value. For instance, quantum mechanics allows a very precise calculation of the half-lives of atoms even though the process of atomic decay is random. More simply, although a single toss of a fair coin cannot be predicted, its general behavior can be described by saying that if a large number of tosses are made, roughly half of them will show up heads. [[Ohm's law]] and the [[Kinetic theory|kinetic theory of gases]] are non-random [[macroscopic]] phenomena that are assumed to be random at the [[microscope|microscopic]] level. == Randomness and religion == Some theologians have attempted to resolve the apparent contradiction between an omniscient deity, or a [[first cause]], and [[free will]] using randomness. [[Discordianism|Discordians]] have a strong belief in randomness and unpredictability. [[Hindu]] and [[Buddhist]] philosophies state that any event is the result of previous events ([[karma]]), and as such, there is no such thing as a random event or a first event. [[Martin Luther]], the forefather of [[Protestantism]], believed that there was nothing random based on his understanding of the [[Bible]]. As an outcome of his understanding of randomness, he strongly felt that free will was limited to low-level decision making by humans. Therefore, when someone sins against another, decision making is only limited to how one responds, preferably through forgiveness and loving actions. He believed, based on Biblical scripture, that humans cannot will themselves faith, salvation, sanctification, or other gifts from God. Additionally, the best people could do, according to his understanding, was not sin, but they fall short, and free will cannot achieve this objective. Thus, in his view, absolute free will and unbounded randomness are severely limited to the point that behaviors may even be patterned or ordered and not random. This is a point emphasized by the field of [[behavioral psychology]]. These notions and more in Christianity often lend to a highly deterministic worldview and that the concept of random events is not possible. Especially, if purpose is part of this universe, then randomness, by definition, is not possible. This is also one of the rationales for religious opposition to [[evolution]], where, according to theory, (non-random) selection is applied to the results of random genetic variation. [[Donald Knuth]], a Stanford computer scientist and Christian commentator, remarks that he finds pseudorandom numbers useful and applies them with purpose. He then extends this thought to God who may use randomness with purpose to allow free will to certain degrees. Knuth believes that God is interested in people's decisions and limited free will allows a certain degree of decision making. Knuth, based on his understanding of [[quantum computing]] and entanglement, comments that God exerts dynamic control over the world without violating any laws of physics, suggesting that what appears to be random to humans may not, in fact, be so random.<ref>Donald Knuth, "Things A Computer Scientist Rarely Talks About", Pg 185, 190-191, CSLI</ref> [[C. S. Lewis]], a 20th-century Christian philosopher, discussed free will at length. On the matter of human will, Lewis wrote: "God willed the free will of men and angels in spite of His knowledge that it could lead in some cases to sin and thence to suffering: i.e., He thought freedom worth creating even at that price." In his radio broadcast, Lewis indicated that God "gave [humans] free will. He gave them free will because a world of mere automata could never love..." In some contexts, procedures that are commonly perceived as randomizers—drawing lots or the like&nbsp;—are used for divination, e.g., to reveal the will of the gods; see e.g. [[Cleromancy]]. == Applications and use of randomness == {{main|Applications of randomness}} In most of its mathematical, political, social and religious use, randomness is used for its innate "fairness" and lack of bias. '''Political''': [[Athenian democracy]] was based on the concept of [[isonomia]] (equality of political rights) and used complex allotment machines to ensure that the positions on the ruling committees that ran Athens were fairly allocated. [[Sortition|Allotment]] is now restricted to selecting jurors in Anglo-Saxon legal systems and in situations where "fairness" is approximated by [[randomization]], such as selecting [[juror]]s and military [[Conscription|draft]] lotteries. '''Social''': Random numbers were first investigated in the context of [[gambling]], and many randomizing devices, such as [[dice]], [[shuffling playing cards]], and [[roulette]] wheels, were first developed for use in gambling. The ability to produce random numbers fairly is vital to electronic gambling, and, as such, the methods used to create them are usually regulated by government [[Gaming Control Board]]s. Random drawings are also used to determine [[lottery]] winners. Throughout history, randomness has been used for games of chance and to select out individuals for an unwanted task in a fair way (see [[drawing straws]]). '''Sports''': Some sports, including [[American Football]], use [[coin toss]]es to randomly select starting conditions for games or [[seed (sports)|seed]] tied teams for [[playoffs|postseason play]]. The [[National Basketball Association]] uses a weighted [[NBA Draft Lottery|lottery]] to order teams in its draft. '''Mathematical''': Random numbers are also used where their use is mathematically important, such as sampling for [[opinion poll]]s and for statistical sampling in [[quality control]] systems. Computational solutions for some types of problems use random numbers extensively, such as in the [[Monte Carlo method]] and in [[genetic algorithm]]s. '''Medicine''': Random allocation of a clinical intervention is used to reduce bias in controlled trials (e.g., [[randomized controlled trials]]). '''Religious''': Although not intended to be random, various forms of [[divination]] such as [[cleromancy]] see what appears to be a random event as a means for a divine being to communicate their will. (See also [[Free will]] and [[Determinism]]). === Generating randomness === {{main|Random number generation}} [[Image:Roulette wheel.jpg|right|200px|thumb|The ball in a [[roulette]] can be used as a source of apparent randomness, because its behavior is very sensitive to the initial conditions.]] It is generally accepted that there exist three mechanisms responsible for (apparently) random behavior in systems: # ''Randomness'' coming from the environment (for example, [[Brownian motion]], but also [[hardware random number generator]]s) # ''Randomness'' coming from the initial conditions. This aspect is studied by [[chaos theory]] and is observed in systems whose behavior is very sensitive to small variations in initial conditions (such as [[pachinko]] machines, [[dice]] ...). # ''Randomness'' intrinsically generated by the system. This is also called [[pseudorandomness]] and is the kind used in [[pseudo-random number generator]]s. There are many algorithms (based on [[arithmetics]] or [[cellular automaton]]) to generate pseudorandom numbers. The behavior of the system can be determined by knowing the [[random seed|seed state]] and the algorithm used. These methods are quicker than getting "true" randomness from the environment. The many [[applications of randomness]] have led to many different methods for generating random data. These methods may vary as to how unpredictable or [[statistical randomness|statistically random]] they are, and how quickly they can generate random numbers. Before the advent of computational [[random number generator]]s, generating large amounts of sufficiently random numbers (important in statistics) required a lot of work. Results would sometimes be collected and distributed as [[random number table]]s. === Randomness measures and tests === There are many practical measures of randomness for a binary sequence. These include measures based on frequency, [[discrete transform]]s, and [[complexity]], or a mixture of these. These include [[Randomness tests|tests]] by Kak, Phillips, Yuen, Hopkins, Beth and Dai, Mund, and Marsaglia and Zaman.<ref>Terry Ritter, Randomness tests: a literature survey. http://www.ciphersbyritter.com/RES/RANDTEST.HTM</ref> == Misconceptions/logical fallacies == {{main|Gambler's fallacy}} Popular perceptions of randomness are frequently mistaken, based on fallacious reasoning or intuitions. === A number is "due" === ''see also [[Coupon collector's problem]] This argument is that "in a random selection of numbers, since all numbers will eventually appear, those that have not come up yet are 'due', and thus more likely to come up soon." This logic is only correct if applied to a system where numbers that come up are removed from the system, such as when [[playing card]]s are drawn and not returned to the deck. In this case, once a jack is removed from the deck, the next draw is less likely to be a jack and more likely to be some other card. However, if the jack is returned to the deck, and the deck is thoroughly reshuffled, a jack is as likely to be drawn as any other card. The same applies in any other process where objects are selected independently, and none are removed after each event, such as the roll of a die, a coin toss, or most [[lottery]] number selection schemes. Truly random processes such as these do not have memory, making it impossible for past outcomes to affect future outcomes. === A number is "cursed" or "blessed" === {{See also|Benford's law}} In a random sequence of numbers, a number may be said to be cursed because it has come up less often in the past, and so it is thought that it will occur less often in the future. A number may be assumed to be blessed because it has occurred more often than others in the past, and so it is thought to be likely to come up more often in the future. This logic is valid only if the randomisation is biased, for example with a loaded die. If the die is fair, then previous rolls give no indication of future events. In nature, events rarely occur with perfectly equal frequency. So observing outcomes to determine which events are likely to have a higher probability, makes sense. It is fallacious to apply this logic to systems which are designed so that all outcomes are equally likely, such as shuffled cards, dice and roulette wheels. === Odds are never dynamic === [[File:Bebes12 008.jpg|thumb|right|120px|Using a probability space, we are less likely to miss one of the possible scenarios, or to neglect the importance of new information]] In the beginning of a scenario, one might calculate the odds of a certain event. The fact is, as soon as one gains more information about that situation, they may need to re-calculate the odds. If we are told that a woman has two children, and one of them is a girl, what are the odds that the other child is also a girl? Considering this new child independently, one might expect the odds that the other child is female are 1/2 (50%). By using mathematician [[Gerolamo Cardano]]'s method of building a [[Probability space]] (illustrating all possible outcomes), we see that the odds are actually only 1/3 (33%). This is because, for starters, the possibility space illustrates 4 ways of having these two children: boy-boy, girl-boy, boy-girl, and girl-girl (assuming the children will be so simply gendered). But we were given more information. Once we are told that one of the children is a female, we use this new information to eliminate the boy-boy scenario. Thus the probability space reveals that there are still 3 ways to have two children where one is a female: boy-girl, girl-boy, girl-girl. Only 1/3 of these scenarios would have the other child also be a girl.<ref name=NYOdds>{{cite news| url=http://www.nytimes.com/2008/06/08/books/review/Johnson-G-t.html?_r=1 | work=The New York Times | first=George | last=Johnson | title=Playing the Odds | date=8 June 2008}}</ref> [[File:Monty open door.svg|thumb|left|100px|When the host reveals that one door only contained a goat, this is new information]] This technique provides insights for other challenges like the [[Monty Hall problem]]. Briefly, it is a game show where the player choses between 3 doors to win a hidden prize like a new car. Once the player choses a door, the host opens another door which reveals only a goat (no prize), and eliminates that door as an option. With only 2 doors left (one with the prize, the other with another goat), the host then asks the player whether they would like to keep the decision they made, or switch their decision to the other door. Intuitively one might think they are simply choosing between 2 doors, and the opportunity provided by the host makes no difference. Probability spaces reveal that one is much better off if they switch their choice after receiving this new information. Cardano's Method reveals what is un-intuitive: at first, the player chose between 3 random doors, and now the player essentially has the opportunity to chose between only 2 random doors.<ref name=NYOdds/> === Ignoring variance === Whether it is a career in poker, as a salesperson, or even searching for the right partner to marry, [[Variance]] and randomness play an important role. Variance sometimes prevents us from drawing causal relationships, even after we have performed multiple experiments - if the experiment is too complex (as it usually is, in day-to-day life). Put simply, in a popular game, some bad players are likely to have winning streaks and good players are likely to have losing streaks. This also explains why [[Coincidences]] should be considered skeptically; rare things, by definition, occasionally happen (e.g. [[2010–2011 midwinter animal mass death events| the sudden death of hundreds of animals]]).<ref name=NYOdds/><ref>{{harvnb|Stanovich|2007}} pg 173</ref> == Books == * ''Randomness'' by Deborah J. Bennett. Harvard University Press, 1998. ISBN 0-674-10745-4. * ''Random Measures, 4th ed.'' by [[Olav Kallenberg]]. Academic Press, New York, London; Akademie-Verlag, Berlin, 1986. MR0854102. * ''The Art of Computer Programming. Vol. 2: Seminumerical Algorithms, 3rd ed.'' by [[Donald Knuth|Donald E. Knuth]]. Reading, MA: Addison-Wesley, 1997. ISBN 0-201-89684-2. * ''[[Fooled by Randomness]], 2nd ed.'' by [[Nassim Nicholas Taleb]]. Thomson Texere, 2004. ISBN 1-58799-190-X. * ''Exploring Randomness'' by [[Gregory Chaitin]]. Springer-Verlag London, 2001. ISBN 1-85233-417-7. * ''Random'' by Kenneth Chan includes a "Random Scale" for grading the level of randomness. == See also == {{wikiversity|Random}} * [[Aleatory]] * [[Chance]] * [[Frequency probability]] * [[Chaitin's constant]] * [[Probability interpretations]] * [[Nonlinear system]] == References == {{reflist}} == External links == {{Wiktionary|randomness}} {{wikiquote}} * [http://www.youtube.com/watch?v=AUSKTk9ENzg An {{convert|8|ft|m|adj=mid|-tall}} Probability Machine (named Sir Francis) comparing stock market returns to the randomness of the beans dropping through the quincunx pattern.] from Index Funds Advisors [http://www.ifa.com IFA.com] * [http://www.quantumlab.de QuantumLab] Quantum random number generator with single photons as interactive experiment. * [http://www.random.org Random.org] generates random numbers using atmospheric noises (see also [[Random.org]]). * [http://www.fourmilab.ch/hotbits/ HotBits] generates random numbers from radioactive decay. * [http://random.irb.hr QRBG] Quantum Random Bit Generator * [http://qrng.physik.hu-berlin.de/ QRNG] Fast Quantum Random Bit Generator * [http://www.cs.auckland.ac.nz/CDMTCS/chaitin/sciamer.html Chaitin: Randomness and Mathematical Proof] * [http://www.fourmilab.ch/random/ A Pseudorandom Number Sequence Test Program (Public Domain)] * [http://etext.lib.virginia.edu/cgi-local/DHI/dhi.cgi?id=dv1-46 ''Dictionary of the History of Ideas'':] Chance * [http://www.spaceandmotion.com/Philosophy-Free-Will-Determinism.htm Philosophy: Free Will vs. Determinism] * [http://www.rahmnation.org RAHM Nation Institute] * [http://www.wolframscience.com/nksonline/page-1067b-text History of randomness definitions], in [[Stephen Wolfram]]'s ''[[A New Kind of Science]]'' * [http://www.cs.auckland.ac.nz/~cristian/Calude361_370.pdf Computing a Glimpse of Randomness] * [http://plato.stanford.edu/entries/chance-randomness/ Chance versus Randomness], from the [[Stanford Encyclopedia of Philosophy]] [[Category:Cryptography]] [[Category:Probability and statistics]] [[Category:Randomness|*]] {{Link GA|es}} [[ar:عشوائية]] [[ca:Atzar]] [[cs:Náhoda]] [[da:Tilfældighed]] [[de:Zufall]] [[es:Aleatoriedad]] [[eo:Hazardo]] [[fi:Satunnaisuus]] [[fr:Hasard]] [[ko:무작위]] [[io:Hazardo]] [[it:Aleatorietà]] [[he:אקראיות]] [[la:Fors]] [[nl:Toeval]] [[ja:ランダム]] [[pl:Losowość]] [[pt:Aleatoriedade]] [[ru:Случайность]] [[simple:Random]] [[sk:Náhodnosť]] [[sv:Slump]] [[zh:随机]]'
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'{{pp-move-indef|small=yes}} {{redirect|Random}} {{selfref|For a random Wikipedia article, see [[Special:Random]]. For information about Wikipedia's random article feature, see [[Wikipedia:Random]].}} {{multiple issues |cleanup=March 2010 |refimprove=March 2010}} '''Randomness''' has somewhat disparate meanings as used in several different fields. It also has common meanings which may have loose connections with some of those more definite meanings. The [[Oxford English Dictionary]] defines "random" thus: <blockquote> Having no definite aim or purpose; not sent or guided in a particular direction; made, done, occurring, etc., without method or conscious choice; haphazard. </blockquote> Closely connected, therefore, with the concepts of chance, [[probability]], and [[information entropy]], randomness implies a lack of [[predictability]]. Randomness is a concept of non-[[:wikt:order|order]] or non-[[:wikt:coherence|coherence]] in a sequence of [[symbol]]s or [[step]]s, such that there is no intelligible pattern or combination. The fields of mathematics, probability, and statistics use formal definitions of randomness. In mathematics, a [[random variable]] is a way to assign a value to each possible outcome of an event. In probability and statistics, a [[random process]] is a repeating process whose outcomes follow no describable [[determinism|deterministic]] pattern, but follow a [[probability distribution]], such that the relative probability of the occurrence of each outcome can be approximated or calculated. For example, the rolling of a fair six-sided die in neutral conditions may be said to produce random results, because one cannot know, before a roll, what number will show up. However, the probability of rolling any one of the six rollable numbers can be calculated. The term is often used in [[statistics]] to signify well-defined statistical properties, such as a lack of [[bias (statistics)|bias]] or [[correlation]]. [[Monte Carlo Method]]s, which rely on random input, are important techniques in science, as, for instance, in [[Scientific computing|computational science]].<ref>[http://www.people.fas.harvard.edu/~junliu/Workshops/workshop2007/ Third Workshop on Monte Carlo Methods], Jun Liu, Professor of Statistics, Harvard University</ref> Random selection is an official method to resolve [[Tie (draw)|tied]] elections in some jurisdictions<ref>Municipal Elections Act (Ontario, Canada) 1996, c. 32, Sched., s. 62 (3) : "If the recount indicates that two or more candidates who cannot both or all be declared elected to an office have received the same number of votes, the clerk shall choose the successful candidate or candidates by lot."</ref> and is even an ancient method of [[divination]], as in [[tarot]], the [[I Ching]], and [[bibliomancy]]. Its use in politics is very old, as office holders in Ancient Athens were chosen by lot, there being no voting. == History == {{Main|History of randomness}} [[File:Pompeii - Osteria della Via di Mercurio - Dice Players.jpg|thumb|Ancient [[fresco]] of dice players in [[Pompei]].]] In ancient history, the concepts of chance and randomness were intertwined with that of fate. Many ancient peoples threw dice to determine fate, and this later evolved into games of chance. Most ancient cultures used various methods of [[divination]] to attempt to circumvent randomness and fate.<ref>''Handbook to life in ancient Rome'' by Lesley Adkins 1998 ISBN 0195123328 page 279</ref><ref>''Religions of the ancient world'' by Sarah Iles Johnston 2004 ISBN 0674015177 page 370</ref> The Chinese were perhaps the earliest people to formalize odds and chance 3,000 years ago. The Greek philosophers discussed randomness at length, but only in non-quantitative forms. It was only in the sixteenth century that Italian mathematicians began to formalize the odds associated with various games of chance. The invention of the [[calculus]] had a positive impact on the formal study of randomness. In the 1888 edition of his book ''The Logic of Chance'' [[John Venn]] wrote a chapter on "The conception of randomness" which included his view of the randomness of the digits of the number [[Pi]] by using them to construct a [[random walk]] in two dimensions.<ref>''Annotated readings in the history of statistics'' by Herbert Aron David, 2001 ISBN 0387988440 page 115. Note that the 1866 edition of Venn's book (on Google books) does not include this chapter.</ref> The early part of the twentieth century saw a rapid growth in the formal analysis of randomness, as various approaches to the mathematical foundations of probability were introduced. In the mid- to late-twentieth century, ideas of [[algorithmic information theory]] introduced new dimensions to the field via the concept of [[algorithmic randomness]]. Although randomness had often been viewed as an obstacle and a nuisance for many centuries, in the twentieth century computer scientists began to realize that the ''deliberate'' introduction of randomness into computations can be an effective tool for designing better algorithms. In some cases such [[randomized algorithms]] outperform the best deterministic methods. == Randomness in science == Many scientific fields are concerned with randomness: * [[Algorithmic probability]] * [[Chaos theory]] * [[Cryptography]] * [[Game theory]] * [[Information theory]] * [[Pattern recognition]] * [[Probability theory]] * [[Quantum mechanics]] * [[Statistics]] * [[Statistical mechanics]] === In the physical sciences === In the 19th century, scientists used the idea of random motions of molecules in the development of [[statistical mechanics]] in order to explain phenomena in [[thermodynamics]] and [[gas laws|the properties of gases]]. According to several standard interpretations of [[quantum mechanics]], microscopic phenomena are objectively random.<ref>[http://www.nature.com/nature/journal/v446/n7138/abs/nature05677.html] in [[Bell's aspect experiment]]: [[Journal: Nature]]</ref> That is, in an experiment where all causally relevant parameters are controlled, there will still be some aspects of the outcome which vary randomly. An example of such an experiment is placing a single unstable [[atom]] in a controlled environment; it cannot be predicted how long it will take for the atom to decay; only the probability of decay within a given time can be calculated.<ref>"Each nucleus decays spontaneously, at random, in accordance with the blind workings of chance". ''Q for Quantum'', [[John Gribbin]]</ref> Thus, quantum mechanics does not specify the outcome of individual experiments but only the probabilities. [[hidden variable theory|Hidden variable theories]] are inconsistent with the view that nature contains irreducible randomness: such theories posit that in the processes that appear random, properties with a certain statistical distribution are somehow at work "behind the scenes" determining the outcome in each case. === In biology === The [[modern evolutionary synthesis]] ascribes the observed diversity of life to [[natural selection]], in which some random genetic [[mutation]]s are retained in the [[gene pool]] due to the ''non-random'' improved chance for survival and reproduction that those mutated genes confer on individuals who possess them. The characteristics of an organism arise to some extent deterministically (e.g., under the influence of genes and the environment) and to some extent randomly. For example, the ''density'' of [[freckles]] that appear on a person's skin is controlled by genes and exposure to light; whereas the exact location of ''individual'' freckles seems to be random.<ref>{{cite journal |last= Breathnach |first= A. S. |year= 1982 |title= A long-term hypopigmentary effect of thorium-X on freckled skin |journal= British Journal of Dermatology |volume= 106 |issue= 1 |pages= 19–25 |doi= 10.1111/j.1365-2133.1982.tb00897.x |quote= The distribution of freckles seems to be entirely random, and not associated with any other obviously punctuate anatomical or physiological feature of skin. |pmid= 7059501}}</ref> Randomness is important if an animal is to behave in a way that is unpredictable to others. For instance, insects in flight tend to move about with random changes in direction, making it difficult for pursuing predators to predict their trajectories. === In mathematics === The mathematical theory of [[probability]] arose from attempts to formulate mathematical descriptions of chance events, originally in the context of [[gambling]], but later in connection with physics. [[Statistics]] is used to infer the underlying [[probability distribution]] of a collection of empirical observations. For the purposes of [[simulation]], it is necessary to have a large supply of [[Random sequence|random numbers]] or means to generate them on demand. [[Algorithmic information theory]] studies, among other topics, what constitutes a [[random sequence]]. The central idea is that a string of [[bit]]s is random if and only if it is shorter than any computer program that can produce that string ([[Kolmogorov randomness]])—this means that random strings are those that cannot be [[data compression|compressed]]. Pioneers of this field include [[Andrey Kolmogorov]] and his student [[Per Martin-Löf]], [[Ray Solomonoff]], and [[Gregory Chaitin]]. In mathematics, there must be an infinite expansion of information for randomness to exist. This can best be seen with an example. Given a random sequence of three-bit numbers, each number can have one of only eight possible values: 000, 001, 010, 011, 100, 101, 110, 111 Therefore, as the random sequence progresses, it must recycle the values it previously used. In order to increase the information space, another bit may be added to each possible number, giving 16 possible values from which to pick a random number. It could be said that the random four-bit number sequence is more random than the three-bit one. This suggests that in order to have true randomness, there must be an infinite expansion of the information space. Randomness is said to occur in numbers such as [[binary logarithm|log (2)]] and [[Pi]]. The decimal digits of Pi constitute an infinite sequence and "never repeat in a cyclical fashion". Numbers like pi are also thought to be [[normal number|normal]], which means that their digits are random in a certain statistical sense. <blockquote> Pi certainly seems to behave this way. In the first six billion decimal places of pi, each of the digits from 0 through 9 shows up about six hundred million times. Yet such results, conceivably accidental, do not prove normality even in base 10, much less normality in other number bases.<ref>[http://www.lbl.gov/Science-Articles/Archive/pi-random.html Are the digits of pi random? researcher may hold the key.]</ref></blockquote> === In statistics === In statistics, randomness is commonly used to create [[simple random samples]]. This allows surveys to be done with completely random groups of people to allow realistic data. Common methods of doing this are "drawing names out of a hat" or using a random digit chart. A random digit chart is simply a large table of random digits. === In information science === In information science, irrelevant or meaningless data is considered to be noise. Noise consists of a large number of transient disturbances with a statistically randomized time distribution. In [[communication theory]], randomness in a signal is called "noise" and is opposed to that component of its variation that is causally attributable to the source, the signal. In terms of the development of random networks, for communication randomness rests on the two simple assumptions of [[Paul Erdős]] and [[Alfréd Rényi]] who said that there were a fixed number of nodes and this number remained fixed for the life of the network, and that all nodes were equal and linked randomly to each other.{{Clarify|date=March 2011}}<ref>Laszso Barabasi, (2003), Linked, Rich Gets Richer, P81</ref> === In finance === The [[random walk hypothesis]] considers that asset prices in an organized [[market]] evolve at random. Other so-called random factors intervene in trends and patterns to do with supply-and-demand distributions. As well as this, the random factor of the environment itself results in fluctuations in stock and broker markets. === Randomness versus unpredictability === Randomness, as opposed to unpredictability, is held to be an objective property - [[determinist]]s believe it is an ''objective'' fact that randomness does not in fact exist. Also, what ''appears'' random to one observer may not appear random to another. Consider two observers of a sequence of bits, when only one of whom has the cryptographic key needed to turn the sequence of bits into a readable message. For that observer the message is not random, but it is unpredictable for the other. One of the intriguing aspects of random processes is that it is hard to know whether a process is truly random. An observer may suspect that there is some "key" that unlocks the message. This is one of the foundations of [[superstition]], but also a motivation for discovery in [[science]] and [[mathematics]]. Under the cosmological hypothesis of [[determinism]], there is no randomness in the universe, only [[predictability|unpredictability]], since there is only one possible outcome to all events in the universe. A follower of the narrow [[frequentist statistics|frequency interpretation of probability]] could assert that no event can be said to have [[probability]], since there is only one universal outcome. On the other hand, under the rival [[Bayesian probability|Bayesian interpretation of probability]] there is no objection to the use of probabilities in order to represent a lack of complete knowledge of the outcomes. Some mathematically defined sequences, such as the decimals of [[pi]] mentioned above, exhibit some of the same characteristics as random sequences, but because they are generated by a describable mechanism, they are called ''[[pseudorandom]]''. To an observer who does not know the mechanism, a pseudorandom sequence is unpredictable. [[chaos theory|Chaotic systems]] are unpredictable in practice due to their extreme sensitivity to initial conditions. Whether or not they are unpredictable in terms of [[computability theory (computation)|computability theory]] is a subject of current research. At least in some disciplines of computability theory, the notion of randomness is identified with computational unpredictability. Individual events that are random may still be precisely described ''en masse'', usually in terms of probability or expected value. For instance, quantum mechanics allows a very precise calculation of the half-lives of atoms even though the process of atomic decay is random. More simply, although a single toss of a fair coin cannot be predicted, its general behavior can be described by saying that if a large number of tosses are made, roughly half of them will show up heads. [[Ohm's law]] and the [[Kinetic theory|kinetic theory of gases]] are non-random [[macroscopic]] phenomena that are assumed to be random at the [[microscope|microscopic]] level. == Randomness and religion == Some theologians have attempted to resolve the apparent contradiction between an omniscient deity, or a [[first cause]], and [[free will]] using randomness. [[Discordianism|Discordians]] have a strong belief in randomness and unpredictability. [[Hindu]] and [[Buddhist]] philosophies state that any event is the result of previous events ([[karma]]), and as such, there is no such thing as a random event or a first event. [[Martin Luther]], the forefather of [[Protestantism]], believed that there was nothing random based on his understanding of the [[Bible]]. As an outcome of his understanding of randomness, he strongly felt that free will was limited to low-level decision making by humans. Therefore, when someone sins against another, decision making is only limited to how one responds, preferably through forgiveness and loving actions. He believed, based on Biblical scripture, that humans cannot will themselves faith, salvation, sanctification, or other gifts from God. Additionally, the best people could do, according to his understanding, was not sin, but they fall short, and free will cannot achieve this objective. Thus, in his view, absolute free will and unbounded randomness are severely limited to the point that behaviors may even be patterned or ordered and not random. This is a point emphasized by the field of [[behavioral psychology]]. These notions and more in Christianity often lend to a highly deterministic worldview and that the concept of random events is not possible. Especially, if purpose is part of this universe, then randomness, by definition, is not possible. This is also one of the rationales for religious opposition to [[evolution]], where, according to theory, (non-random) selection is applied to the results of random genetic variation. [[Donald Knuth]], a Stanford computer scientist and Christian commentator, remarks that he finds pseudorandom numbers useful and applies them with purpose. He then extends this thought to God who may use randomness with purpose to allow free will to certain degrees. Knuth believes that God is interested in people's decisions and limited free will allows a certain degree of decision making. Knuth, based on his understanding of [[quantum computing]] and entanglement, comments that God exerts dynamic control over the world without violating any laws of physics, suggesting that what appears to be random to humans may not, in fact, be so random.<ref>Donald Knuth, "Things A Computer Scientist Rarely Talks About", Pg 185, 190-191, CSLI</ref> [[C. S. Lewis]], a 20th-century Christian philosopher, discussed free will at length. On the matter of human will, Lewis wrote: "God willed the free will of men and angels in spite of His knowledge that it could lead in some cases to sin and thence to suffering: i.e., He thought freedom worth creating even at that price." In his radio broadcast, Lewis indicated that God "gave [humans] free will. He gave them free will because a world of mere automata could never love..." In some contexts, procedures that are commonly perceived as randomizers—drawing lots or the like&nbsp;—are used for divination, e.g., to reveal the will of the gods; see e.g. [[Cleromancy]]. == Applications and use of randomness == {{main|Applications of randomness}} In most of its mathematical, political, social and religious use, randomness is used for its innate "fairness" and lack of bias. '''Political''': [[Athenian democracy]] was based on the concept of [[isonomia]] (equality of political rights) and used complex allotment machines to ensure that the positions on the ruling committees that ran Athens were fairly allocated. [[Sortition|Allotment]] is now restricted to selecting jurors in Anglo-Saxon legal systems and in situations where "fairness" is approximated by [[randomization]], such as selecting [[juror]]s and military [[Conscription|draft]] lotteries. '''Social''': Random numbers were first investigated in the context of [[gambling]], and many randomizing devices, such as [[dice]], [[shuffling playing cards]], and [[roulette]] wheels, were first developed for use in gambling. The ability to produce random numbers fairly is vital to electronic gambling, and, as such, the methods used to create them are usually regulated by government [[Gaming Control Board]]s. Random drawings are also used to determine [[lottery]] winners. Throughout history, randomness has been used for games of chance and to select out individuals for an unwanted task in a fair way (see [[drawing straws]]). '''Sports''': Some sports, including [[American Football]], use [[coin toss]]es to randomly select starting conditions for games or [[seed (sports)|seed]] tied teams for [[playoffs|postseason play]]. The [[National Basketball Association]] uses a weighted [[NBA Draft Lottery|lottery]] to order teams in its draft. '''Mathematical''': Random numbers are also used where their use is mathematically important, such as sampling for [[opinion poll]]s and for statistical sampling in [[quality control]] systems. Computational solutions for some types of problems use random numbers extensively, such as in the [[Monte Carlo method]] and in [[genetic algorithm]]s. '''Medicine''': Random allocation of a clinical intervention is used to reduce bias in controlled trials (e.g., [[randomized controlled trials]]). '''Religious''': Although not intended to be random, various forms of [[divination]] such as [[cleromancy]] see what appears to be a random event as a means for a divine being to communicate their will. (See also [[Free will]] and [[Determinism]]). === Generating randomness === {{main|Random number generation}} [[Image:Roulette wheel.jpg|right|200px|thumb|The ball in a [[roulette]] can be used as a source of apparent randomness, because its behavior is very sensitive to the initial conditions.]] It is generally accepted that there exist three mechanisms responsible for (apparently) random behavior in systems: # ''Randomness'' coming from the environment (for example, [[Brownian motion]], but also [[hardware random number generator]]s) # ''Randomness'' coming from the initial conditions. This aspect is studied by [[chaos theory]] and is observed in systems whose behavior is very sensitive to small variations in initial conditions (such as [[pachinko]] machines, [[dice]] ...). # ''Randomness'' intrinsically generated by the system. This is also called [[pseudorandomness]] and is the kind used in [[pseudo-random number generator]]s. There are many algorithms (based on [[arithmetics]] or [[cellular automaton]]) to generate pseudorandom numbers. The behavior of the system can be determined by knowing the [[random seed|seed state]] and the algorithm used. These methods are quicker than getting "true" randomness from the environment. The many [[applications of randomness]] have led to many different methods for generating random data. These methods may vary as to how unpredictable or [[statistical randomness|statistically random]] they are, and how quickly they can generate random numbers. Before the advent of computational [[random number generator]]s, generating large amounts of sufficiently random numbers (important in statistics) required a lot of work. Results would sometimes be collected and distributed as [[random number table]]s. === Randomness measures and tests === There are many practical measures of randomness for a binary sequence. These include measures based on frequency, [[discrete transform]]s, and [[complexity]], or a mixture of these. These include [[Randomness tests|tests]] by Kak, Phillips, Yuen, Hopkins, Beth and Dai, Mund, and Marsaglia and Zaman.<ref>Terry Ritter, Randomness tests: a literature survey. http://www.ciphersbyritter.com/RES/RANDTEST.HTM</ref> == Misconceptions/logical fallacies == {{main|Gambler's fallacy}} Popular perceptions of randomness are frequently mistaken, based on fallacious reasoning or intuitions. === A number is "due" === ''see also [[Coupon collector's problem]] This argument is that "in a random selection of numbers, since all numbers will eventually appear, those that have not come up yet are 'due', and thus more likely to come up soon." This logic is only correct if applied to a system where numbers that come up are removed from the system, such as when [[playing card]]s are drawn and not returned to the deck. In this case, once a jack is removed from the deck, the next draw is less likely to be a jack and more likely to be some other card. However, if the jack is returned to the deck, and the deck is thoroughly reshuffled, a jack is as likely to be drawn as any other card. The same applies in any other process where objects are selected independently, and none are removed after each event, such as the roll of a die, a coin toss, or most [[lottery]] number selection schemes. Truly random processes such as these do not have memory, making it impossible for past outcomes to affect future outcomes. === A number is "cursed" or "blessed" === {{See also|Benford's law}} In a random sequence of numbers, a number may be said to be cursed because it has come up less often in the past, and so it is thought that it will occur less often in the future. A number may be assumed to be blessed because it has occurred more often than others in the past, and so it is thought to be likely to come up more often in the future. This logic is valid only if the randomisation is biased, for example with a loaded die. If the die is fair, then previous rolls give no indication of future events. In nature, events rarely occur with perfectly equal frequency. So observing outcomes to determine which events are likely to have a higher probability, makes sense. It is fallacious to apply this logic to systems which are designed so that all outcomes are equally likely, such as shuffled cards, dice and roulette wheels. === Odds are never dynamic === [[File:Bebes12 008.jpg|thumb|right|120px|Using a probability space, we are less likely to miss one of the possible scenarios, or to neglect the importance of new information]] In the beginning of a scenario, one might calculate the odds of a certain event. The fact is, as soon as one gains more information about that situation, they may need to re-calculate the odds. If we are told that a woman has two children, and one of them is a girl, what are the odds that the other child is also a girl? Considering this new child independently, one might expect the odds that the other child is female are 1/2 (50%). By using mathematician [[Gerolamo Cardano]]'s method of building a [[Probability space]] (illustrating all possible outcomes), we see that the odds are actually only 1/3 (33%). This is because, for starters, the possibility space illustrates 4 ways of having these two children: boy-boy, girl-boy, boy-girl, and girl-girl (assuming the children will be so simply gendered). But we were given more information. Once we are told that one of the children is a female, we use this new information to eliminate the boy-boy scenario. Thus the probability space reveals that there are still 3 ways to have two children where one is a female: boy-girl, girl-boy, girl-girl. Only 1/3 of these scenarios would have the other child also be a girl.<ref name=NYOdds>{{cite news| url=http://www.nytimes.com/2008/06/08/books/review/Johnson-G-t.html?_r=1 | work=The New York Times | first=George | last=Johnson | title=Playing the Odds | date=8 June 2008}}</ref> [[File:Monty open door.svg|thumb|left|100px|When the host reveals that one door only contained a goat, this is new information]] This technique provides insights for other challenges like the [[Monty Hall problem]]. Briefly, it is a game show where the player choses between 3 doors to win a hidden prize like a new car. Once the player choses a door, the host opens another door which reveals only a goat (no prize), and eliminates that door as an option. With only 2 doors left (one with the prize, the other with another goat), the host then asks the player whether they would like to keep the decision they made, or switch their decision to the other door. Intuitively one might think they are simply choosing between 2 doors, and the opportunity provided by the host makes no difference. Probability spaces reveal that one is much better off if they switch their choice after receiving this new information. Cardano's Method reveals what is un-intuitive: at first, the player chose between 3 random doors, and now the player essentially has the opportunity to chose between only 2 random doors.<ref name=NYOdds/> === Ignoring variance === Whether it is a career in poker, as a salesperson, or even searching for the right partner to marry, [[Variance]] and randomness play an important role. Variance sometimes prevents us from drawing causal relationships, even after we have performed multiple experiments - if the experiment is too complex (as it usually is, in day-to-day life). Put simply, in a popular game, some bad players are likely to have winning streaks and good players are likely to have losing streaks. This also explains why [[Coincidences]] should be considered skeptically; rare things, by definition, occasionally happen (e.g. [[2010–2011 midwinter animal mass death events| the sudden death of hundreds of animals]]).<ref name=NYOdds/><ref>{{harvnb|Stanovich|2007}} pg 173</ref>One example is NOAH RULES!!!!!!!! this is random. == Books == * ''Randomness'' by Deborah J. Bennett. Harvard University Press, 1998. ISBN 0-674-10745-4. * ''Random Measures, 4th ed.'' by [[Olav Kallenberg]]. Academic Press, New York, London; Akademie-Verlag, Berlin, 1986. MR0854102. * ''The Art of Computer Programming. Vol. 2: Seminumerical Algorithms, 3rd ed.'' by [[Donald Knuth|Donald E. Knuth]]. Reading, MA: Addison-Wesley, 1997. ISBN 0-201-89684-2. * ''[[Fooled by Randomness]], 2nd ed.'' by [[Nassim Nicholas Taleb]]. Thomson Texere, 2004. ISBN 1-58799-190-X. * ''Exploring Randomness'' by [[Gregory Chaitin]]. Springer-Verlag London, 2001. ISBN 1-85233-417-7. * ''Random'' by Kenneth Chan includes a "Random Scale" for grading the level of randomness. == See also == {{wikiversity|Random}} * [[Aleatory]] * [[Chance]] * [[Frequency probability]] * [[Chaitin's constant]] * [[Probability interpretations]] * [[Nonlinear system]] == References == {{reflist}} == External links == {{Wiktionary|randomness}} {{wikiquote}} * [http://www.youtube.com/watch?v=AUSKTk9ENzg An {{convert|8|ft|m|adj=mid|-tall}} Probability Machine (named Sir Francis) comparing stock market returns to the randomness of the beans dropping through the quincunx pattern.] from Index Funds Advisors [http://www.ifa.com IFA.com] * [http://www.quantumlab.de QuantumLab] Quantum random number generator with single photons as interactive experiment. * [http://www.random.org Random.org] generates random numbers using atmospheric noises (see also [[Random.org]]). * [http://www.fourmilab.ch/hotbits/ HotBits] generates random numbers from radioactive decay. * [http://random.irb.hr QRBG] Quantum Random Bit Generator * [http://qrng.physik.hu-berlin.de/ QRNG] Fast Quantum Random Bit Generator * [http://www.cs.auckland.ac.nz/CDMTCS/chaitin/sciamer.html Chaitin: Randomness and Mathematical Proof] * [http://www.fourmilab.ch/random/ A Pseudorandom Number Sequence Test Program (Public Domain)] * [http://etext.lib.virginia.edu/cgi-local/DHI/dhi.cgi?id=dv1-46 ''Dictionary of the History of Ideas'':] Chance * [http://www.spaceandmotion.com/Philosophy-Free-Will-Determinism.htm Philosophy: Free Will vs. Determinism] * [http://www.rahmnation.org RAHM Nation Institute] * [http://www.wolframscience.com/nksonline/page-1067b-text History of randomness definitions], in [[Stephen Wolfram]]'s ''[[A New Kind of Science]]'' * [http://www.cs.auckland.ac.nz/~cristian/Calude361_370.pdf Computing a Glimpse of Randomness] * [http://plato.stanford.edu/entries/chance-randomness/ Chance versus Randomness], from the [[Stanford Encyclopedia of Philosophy]] [[Category:Cryptography]] [[Category:Probability and statistics]] [[Category:Randomness|*]] {{Link GA|es}} [[ar:عشوائية]] [[ca:Atzar]] [[cs:Náhoda]] [[da:Tilfældighed]] [[de:Zufall]] [[es:Aleatoriedad]] [[eo:Hazardo]] [[fi:Satunnaisuus]] [[fr:Hasard]] [[ko:무작위]] [[io:Hazardo]] [[it:Aleatorietà]] [[he:אקראיות]] [[la:Fors]] [[nl:Toeval]] [[ja:ランダム]] [[pl:Losowość]] [[pt:Aleatoriedade]] [[ru:Случайность]] [[simple:Random]] [[sk:Náhodnosť]] [[sv:Slump]] [[zh:随机]]'
Whether or not the change was made through a Tor exit node (tor_exit_node)
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