At the heart of any Monte Carlo method are two fundamental building blocks. The first is the generation of uniform random variables. The second is the transformation of the generated uniform distribution to whichever is the required distribution for the task at hand. For example, a normal one. The quality of random number generator determines the quality of the final distribution.

No matter how good is the underlying random number generation algorithm, the outcome is not truly random, since the algorithm that produces the numbers is fully deterministic. In fact, I actually believe that very few things in live are truly random. Even the seemingly random roulette wheel is influenced by the physical properties such as friction, weight and skewness. So, let’s embrace the fact that we’ll only ever be able to achieve pseudo-randomness. This should be good enough for all practical purposes. Thus, I will stop using ‘pseudo-random’, and use the word ‘random’ instead.

In this post I will show you some very basic algorithms for generating random numbers. If you do further research, you will find that the more complex algorithms are extensions of the basic ones.

Take int x = 1. We can use x to generate a next random number. How? Well, take, for example:

x_next = (25,173*x+13,849) %201

where % is the modulus operator. x_next is (also an integer) = 28. Ok, 28 is kind-of random. Let’s repeat the above calculation where x=28. x_next becomes 118. Hm, if I keep repeating the same ‘substitute/calculate’ steps 10 times, I get:

28, 118, 16, 145, 106, 43, 34, 4, 172, 196

Well, that looks pretty random to me! I use this algorithm to generate 200 integers and show them in Figure 1 (hint: click on it to see the details).

Before I point out a problem with this random sample, let me explain what is going on here. The way I generated my random variables is called linear congruential generator, originally introduced by D.H.Lehmer in 1951. It has a general form of

The above will produce a sequence of random numbers in the region between 1 and m-1. If the required uniform variables should be in the unit interval, we can scale by 1/m. Referring back to our initial example, a, c and m are some constants. It turns out that the values of these constants are critical in determining the quality of the generated random variable sequence. Looking at Figure 1 you should be able to spot that the random sequence repeats itself exactly after only 65 iterations. Clearly, this is not a very good random number generator! Ideally, we need an algorithm that can simulate all m-1 random variables without repeating itself. This condition is known as ‘having a full period length’, and it is one of the standard criteria for selecting a random number generator.

In (1), we find the list of conditions a, c and m must satisfy to ensure we obtain a full period length (if c!=0):

• c and m are relatively prime (their only common divisor is 1);
• every prime number that divides m, also divides a-1;
• a-1 is divisible by 4, if m is;

So, the reason we did not obtain the full period length is because the chosen constants do not meet these conditions. If we change m to be 217, it allows for a full period of length 216.

The demonstrated random number generator is the basis for more complex generators. A good random number generator should be able to generate millions of numbers without repeating the sequence. If we are to stick to the int representation, we have to take care not to overflow. Reminder: a System.Int32 variable can hold a maximum value of [(2^32)-1]/2-1.

Many various extensions to the linear congruential generator have been developed over the years; many involving a combination of several linear ones which result in a much longer period length. In C#, the System.Random class provides an easy way to generate random variables. The full implementation of this class can be found here. Random class can be initialised with a given seed, or without. When no seed is provided, the default seed is Environment.TickCount, which is the time in milliseconds since the computer been booted-up. The algorithm behind System.Random is based on the routine in “Numerical Recipes In C: The Art Of Scientific Computing”, which, according to the authors, has been proposed by D.Knuth. The algorithm has elements of the linear congruential generator, but is essentially a mixed ‘subtractive method’ where an array of 55 pseudo-random integers is maintained. At the class initialization, the array is populated with values twice. First, the array is populated with values between 0 and [max(System.Int32)-1]. At this point, the index of the array which receives the value is selected using

This is ‘the element’ of the linear congruential generator I’ve mentioned above. Here i is in [1,54]. Note that c is set to zero, and the three conditions listed above do not apply. The second time the array is re-populated, it’s values are further randomized by cycling through the array four times and reassigning each value with a difference between its original value and a value at another index, which is approximately half-the-array length away. Positivity is ensured by adding max(System.Int32) to any negative number.

Every time a user asks for the next random number, the difference between two values in the array is returned. At the same time, one of the values in the array is replaced with the value returned to the user. There is a check for positivity, which is ensured by adding max(System.Int32) to any negative number.

The Random class has additional methods to allow for simulating random numbers in some range or even simulating random doubles. The latter returns a a double equal to a randomly generated integer scaled by (1/ max(System.Int32)).

 
References:
(1) P. Glasserman. “Monte Carlo Methods in Financial Engineering”, 2004. Springer-Verlag.