Page 80 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes Some applications which appear at first sight to be suitable for randomization are in fact not
quite so simple. For instance, a system that ‘randomly’ selects music tracks for a background
music system must only appear to be random; a true random system would have no restriction
on the same item appearing two or three times in succession.

Did u know? The Meaning of Cryptography
The word is obtained from the Greek kryptos, meaning hidden. Cryptography comprises
techniques like microdots, merging words with images, and other methods to hide
information in storage or transfer.

Activities and Demonstrations

The SOCR resource pages contain a number of hands-on interactive activities and demonstrations
of random number generation using Java applets.

“True” Random Numbers vs. Pseudorandom Numbers

There are two principal methods used to generate random numbers. One measures some physical
phenomenon that is expected to be random and then compensates for possible biases in the
measurement process. The other uses computational algorithms that produce long sequences of
apparently random results, which are in fact completely determined by a shorter initial value,
known as a seed or key. The latter types are often called pseudorandom number generators.

A “random number generator” based solely on deterministic computation cannot be regarded
as a “true” random number generator, since its output is inherently predictable. John von
Neumann famously said “Anyone who uses arithmetic methods to produce random numbers is
in a state of sin.” How to distinguish a “true” random number from the output of a pseudo-
random number generator is a very difficult problem. However, carefully chosen pseudo-
random number generators can be used instead of true random numbers in many applications.
Rigorous statistical analysis of the output is often needed to have confidence in the algorithm.
Generating Random Numbers from Physical Processes

There is general agreement that, if there are such things as “true” random numbers, they are
most likely to be found by looking at physical processes which are, as far as is known,
unpredictable.
A physical random number generator can be based on an essentially random atomic or subatomic
physical phenomenon whose unpredictability can be traced to the laws of quantum mechanics.

Example of this is the Atari 8-bit computers, which used electronic noise from an analog
circuit to generate true random numbers. Other examples include radioactive decay, thermal
noise, shot noise and clock drift. Even lava lamps have been used by the Lava rand generator.
To provide a degree of randomness intermediate between specialized hardware on the one
hand and algorithmic generation on the other, some security related computer software requires
the user to input a lengthy string of mouse movements, or keyboard input.

Post-processing and Statistical Checks

Even given a source of plausible random numbers (perhaps from a quantum mechanically
based hardware generator), obtaining numbers which are completely unbiased takes care. In

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