Correct. Class Random is not absolutely totally random. The important question is, is it as statistically close to being random as you need it to be. The output from class Random is statistically as nearly random as a reasonable deterministic program can be. The algorithm uses a 48-bit seed modified by a linear congruential formula. If the Random object is created using the parameterless constructor, the 48 low-order bits of milli-second time get used as the seed. If the Random object is created using the seed parameter (a long), the 48 low-order bits of the long get used as the seed.
If Random is instanced with the same seed and make the exact same sequence of next calls are made from it, the exact same sequence of values will result from that instance. This is deliberate to allow for predictable software testing and dmonstrations. Ordinarliy, Random is not used with a constant seed for operational use since it is usually used to get unpredictable psuedo-random sequences. If two instances of Random with the parameterless constructors get created in the same clock millisecond, they will also get the same sequences from both instances. It is important to note that eventually, a Random instance will repeat its pattern. Therefore, a Random instance should not be used for enormously long sequences before creating a new instance.
There is no reason not to use the Random class except for high-security cryptographic applications or some special need where some aspect of true randomness is of paramount importance, something that is uncommon. In those cases, you really need a hardware randomizer that uses radioactive decay or infinitesimal molecular level brownian motion induced randomness to generate a random result. Sun SPARC hardware platforms had such hardware installable. Other platforms can have them too, along with the hardware drivers that give access to the randomness they generate.
The algorithm used in class Random is the result of considerable research by some of the best minds in computer science and mathematics. Given the right parameters, it provides remarkable and outstanding results. Other more recent algorithms may be better for some limited applications, but they also have performance or specific application issues that make them less suitible for general purpose use. The linear congruential algorithm still remains one of the most widely used general purpose pseudo-random number generators.
The following quote is from Donald Knuth's book, The Art of Computer Programming, Volume 2, Semi-numerical Algorithms, Section 3.2.1. The quote describes the linear congruential method and discusses its properties. If you don't know who Donald Knuth is or have never read any of his papers or books, he, amongst other things, showed that there can be no sort faster than Tony Hoare's Quicksort with partion pivot strategies created by Robert Sedgewick. Robert Sedgewick, who suggested the best simple pivot selection strategies for Quicksort, did his doctoral thesis on Quicksort under Donald Knuth's supervision. Knuth's multi-volume work, The Art Of Computer Programming, is one of the greatest expositions of the most important theoretical aspects of computing ever assembled, including sorting, searching and randomizing algorithms. There is a lot of discussion in Chapter 3 of this about what randomness really is, statistically and philosophically, and about software that emmulates true randomness to the point where it is statistically nearly indistinguishable from it for very large, but still finite, sequences. What follows is pretty heavy reading:
3.2.1. The Linear Congruential Method
By far the most popular random number generators in use today are special cases of the following
scheme, introduced by D. H. Lehmer in 1949. [See Proc. 2nd Symp. on
Large-Scale Digital Calculating Machinery (Cambridge, Mass.: Harvard
University Press, 1951), 141-146.]
We choose four magic integers:
m, the modulus; 0 < m.
a, the multiplier; 0 <= a < m.
c, the increment; 0 <= c < m.
X[0], the starting value; 0 <= X[0] < m. (equation 1)
The desired sequence of random numbers (X[n] ) is then obtained by setting
X[n+1] = (a * X[n] + c) mod m, n >= O. (equation 2)
This is called a linear congruential sequence. Taking the remainder mod m is somewhat
like determining where a ball will land in a spinning roulette wheel.
For example, the sequence obtained when m == 10 and X[0] == a == c == 7 is
7, 6, 9, 0, 7, 6, 9, 0, ... . (example 3)
As this example shows, the
sequence is not always "random" for all choices of m, a, c, and X[0];
the principles of choosing the magic numbers appropriately will be
investigated carefully in later parts of this chapter.
Example (3)
illustrates the fact that the congruential sequences always get into
a loop: There is ultimately a cycle of numbers that is repeated
endlessly. This property is common to all sequences having the
general form X[n+1] = f(X[n]), when f transforms a finite set into
itself; see exercise 3.1-6. The repeating cycle is called the period;
sequence (3) has a period of length 4. A useful sequence will of
course have a relatively long period.
The special case c == 0 deserves
explicit mention, since the number generation process is a little
faster when c == 0 than it is when c != O. We shall see later that the
restriction c == 0 cuts down the length of the period of the sequence,
but it is still possible to make the period reasonably long. Lehmer's
original
generation method had c == 0, although he mentioned c != 0 as a possibility; the fact that c
!= 0 can lead to longer periods is due to Thomson [Compo J. 1 (1958), 83, 86] and, independently, > to Rotenberg [JACM 7 (1960), 75-77]. The
terms multiplicative congruential method and mixed congruential
method are used by many authors to denote linear congruential
sequences with c == 0 and c != 0, respectively.
The letters m, a, c,
and X[0] will be used throughout this chapter in the sense described
above. Furthermore, we will find it useful to define
b = a - 1, (equation 4)
in order to simplify many of our formulas.
We can immediately reject the case a == 1, for this would mean that X[n] = (X[0]
+ n * c) mod m, and the sequence would certainly not behave as a random sequence. The case a == 0 is even worse. Hence for practical purposes
we may assume that
a >= 2, b >= 1. (equation 5)
Now we can prove a generalization of Eq. (2),
X[n+k] = (a^k * X[n] + (a^k - 1) * c / b) mod m, k >= 0, n >= 0,
(equation 6)
which expresses the (n+k)th term directly in terms of the nth
term. (The special case n == 0 in this equation is worthy of note.) It
follows that the subsequence consisting of every kth term of (X[n])
is another linear congruential sequence, having the multiplier a k
mod m and the increment ((a^k - 1) * c / b) mod m.
An important corollary
of (6) is that the general sequence defined by m, a, c, and X[0] can be
expressed very simply in terms of the special case where c == 1 and X[0]
== O. Let
Y[0] = 0, Y[n+1] = (a * Y[n+1]) mod m. (equation 7)
According to Eq. (6) we will have Y[k] === (a^k - 1) / b(modulo m), hence the general
sequence defined in (2) satisfies
X[n] = (A * Y[n] + X[0]) mod m, where A == (X[0] * b + c) mod m. (equation 8)
