arc4random() and arc4random_uniform() not really random?

It depends on what you mean when you say random.

As stated in the comments, true randomness is clumpy. Long strings of repeats or close values are expected.

If this doesn't fit your requirement, then you need to better define your requirement.

Other options could include using a shuffle algorithm to dis-order things in an array, or use an low-discrepancy sequence algorithm to give a equal distribution of values.

I don’t really agree with the idea of humans who are very bad to detect randomness. Would you be satisfied if you obtain 1-1-2-2-3-3-4-4-5-5-6-6 after throwing 6 couples of dices ? however the dices frequencies are perfect…

This is exactly the problem i’m encountering with arc4random or arc4random_uniform functions. I’m developing a backgammon application since many years which is based on a neural network trained by word champions players. I DO know that it plays much better than any one but many users think it is cheating. I also have doubts sometimes so I’ve decided to throw all dices by myself…

I’m not satisfied at all with arc4random, even if frequencies are OK. I always throw a couple of dices and results lead to unacceptable situations, for example : getting five consecutive double dices for the same player, waiting 12 turns (24 dices) until the first 6 occurs.

It is easy to test (C code) :

void randomDices ( int * dice1, int * dice2, int player )
{
    ( * dice1 ) = arc4random_uniform ( 6 ) ;
    ( * dice2 ) = arc4random_uniform ( 6 ) ;

    // Add to your statistics
    [self didRandomDice1:( * dice1 ) dice2:( * dice2 )  forPlayer:player] ;
}

Maybe arc4random doesn’t like to be called twice during a short time…

So I’ve tried several solutions and finally choose this code which runs a second level of randomization after arc4random_uniform :

int CFRandomDice ()
{
    int __result = -1 ;

    BOOL __found = NO ;

    while ( ! __found )
    {
        // random int big enough but not too big
        int __bigint = arc4random_uniform ( 10000 ) ;

        // Searching for the first character between '1' and '6'
        // in the string version of bigint :

        NSString * __bigString = @( __bigint ).stringValue ;

        NSInteger __nbcar = __bigString.length ;
        NSInteger __i = 0 ;

        while ( ( __i < __nbcar ) && ( ! __found ) )
        {
            unichar __ch = [__bigString characterAtIndex:__i] ;
            if ( ( __ch >= '1' ) && ( __ch <= '6' ) )
            {
                __found = YES ;
                __result = __ch - '1' + 1 ;
            }
            else
            {
                __i++ ;
            }
        }
    }

    return ( __result ) ;
}

This code create a random number with arc4random_uniform ( 10000 ), convert it to string and then searches for the first digit between ‘1’ and ‘6’ in the string.

This appeared to me as a very good way to randomize the dices because : 1/ frequencies are OK (see the statistics hereunder) ; 2/ Exceptional dice sequences occur at exceptional times.

10000 dices test:
----------
Game Stats
----------
HIM :
Total 1 = 3297
Total 2 = 3378
Total 3 = 3303
Total 4 = 3365
Total 5 = 3386
Total 6 = 3271
----------
ME :
Total 1 = 3316
Total 2 = 3289
Total 3 = 3282
Total 4 = 3467
Total 5 = 3236
Total 6 = 3410
----------
HIM doubles = 1623
ME doubles = 1648

Now I’m sure that players won’t complain…

A true random sequence of numbers cannot be generated by an algorithm. They can only produce pseudo-random sequence of numbers (something that looks like a random sequence). So depending on the algorithm chosen, the quality of the "randomness" may vary. The quality of arc4random() sequences is generally considered to have a good randomness.

You cannot analyze the randomness of a sequence visually... Humans are very bad to detect randomness! They tend to find some structure where there is no. Nothing really hurts in your diagrams (except the rare subsequence of 6 three in-a-row, but that is randomness, sometimes unusual things happens). You would be surprised if you had used a dice to generate a sequence and draw its graph. Beware that a sample of only 20 numbers cannot be seriously analyzed against its randomness, your need much bigger samples.

If you need some other kind of randomness, you can try to use /dev/random pseudo-file, which generate a random number each time you read in. The sequence is generated by a mix of algorithms and external physical events that ay happens in your computer.