RNG / SHUFFLING
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Random Number Generation and Shuffling
A critical aspect of any card game is the random shuffle of
the deck. We take this very seriously and want to share with
you our approach. The two critical aspects are the use of
unpredictable random numbers, and the approach to ensuring a
random and unpredictable ordering of the deck.
We use the random number generator (RNG) available from the Linux
operating system. This is an entropy based RNG.
The random number generator uses (collects) noise from the system,
device drivers and other sources such as network activity and CPU
utilization, into an entropy pool. From this entropy pool random
numbers are created. Linux provides the /dev/random device.
Reading this device from code provides random numbers.
Our approach is to take seeds from that pool on a periodic basis.
These seeds serve as the starting number for an elementary cellular
automaton. A cellular automaton in our case is an array of
binary values. A complex rule for state transitions creates new
random numbers from the seed value. We use a rule known to produce
randomness. "Rule 30 is of special interest because it is chaotic Φ
this rule is used as the random number generator used for large
integers in Mathematica."
Once we have that seed, we produce random number through the state
transitions of the cellular automaton. Our cellular automaton has
256 bits. We take the additional step of randomly selecting one
(1) bit from each of the successive 255 rows (states) to produce
our random numbers.
Our random number generator passed two (2) standard tests: the
Diehard and the ENT
 tests. More importantly, we our code
has been inspected and certified by an BMM  independent testing
lab specializing in the analysis, review, and certification of
software for gaming and cryptology
A random number generator is only part of the story in producing
randomly distributed decks of cards. How the deck is shuffled
is equally important. We have selected an algorithm (process)
that ensures a true randomizing shuffle. This approach does not
favor any one card or any one position in the deck.
We then assign each card a 256 bit random number. This very
large number is greater than 1 with 77 zeros after it. With
each card given its own huge random number, we sort the deck
by those random numbers, placing the cards in yet another random
sequence. This algorithm also ensures that every possible card
sequence is theoretically equally likely to occur.
Our approach relies on well-accepted random number generation
tools, relying on system entropy to generate random seeds. We
then use a system known to generate randomly distributed numbers
from the seed number. As a further security step, we periodically
select a new seed. Our decks are randomized prior to first use,
and then serve as the random base from which a randomizing shuffle
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