Oh Sophie, how clever you are!

as some one who does a lot of work with fractals I can see how it works very well…

emergent order in complexity theory: stuff that makes sense.

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 the properties of gases.

According to several standard interpretations of quantum mechanics, microscopic phenomena are objectively random. 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.[3] Thus quantum mechanics does not specify the outcome of individual experiments but only the probabilities. 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.

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

Humankind has been concerned with random physical processes since pre-historic times. Examples are divination (cleromancy, reading messages in casting lots), the use of allotment in the Athenian democracy, and the frequent references to the casting of lots found in the Old Testament.

Despite the prevalence of gambling in all times and cultures, for a long time there was little inquiry into the subject. Though Gerolamo Cardano and Galileo wrote about games of chance, the first mathematical treatments were given by Blaise Pascal, Pierre de Fermat and Christiaan Huygens. The classical version of probability theory that they developed proceeds from the assumption that outcomes of random processes are equally likely; thus they were among the first to give a definition of randomness in statistical terms. The concept of statistical randomness was later developed into the concept of information entropy in information theory.

In the early 1960s Gregory Chaitin, Andrey Kolmogorov and Ray Solomonoff introduced the notion of algorithmic randomness, in which the randomness of a sequence depends on whether it is possible to compress it.

Randomness is a lack of order, purpose, cause, or predictability. A random process is a repeating process whose outcomes follow no describable deterministic pattern, but follow a probability distribution.

The term is often used in statistics to signify well-defined statistical properties, such as a lack of bias or correlation. Monte Carlo Methods, which rely on random input, are important techniques of computational science.[1] Random selection is an official method to resolve tied elections in some jurisdictions[2], and is even an ancient method of divination, as in tarot, the I Ching, and bibliomancy.