What's the importance of using Random.setSeed?

Question

When writing Java program, we use setSeed in the Random class. Why would we use this method?

Can't we just use Random without using setSeed? What is the main purpose of using setSeed?

Answer 1

A specific seed will always give the same sequence of "pseudo-random" numbers. So there are only 2^48 different sequences in Random because setSeed only uses 48-bits of the seed parameter! Besides setSeed, one may also use a constructor with a seed (e.g. new Random(seed)).

When setSeed(seed) or new Random(seed) are not used, the Random() constructor sets the seed of the random number generator to a value very likely to be distinct from any other invocation of this constructor.

Java reference for the above information: https://docs.oracle.com/en/java/javase/11/docs/api/java.base/java/util/Random.html

In the ordinary case, don't use a seed. Just use the empty constructor Random() and don't call setSeed. This way you'll likely get different pseudo-random numbers each time the class is constructed and invoked.

For data dependent debugging, where you want to repeat the same pseudo-random numbers, use a specific seed. In this case, use Random(seed) or setSeed(seed).

For non-security critical uses, there's no need to worry whether the specific seed/sequence might be recognized and subsequent numbers predicted, because of the large range of seeds. However, "Instances of java.util.Random are not cryptographically secure. Consider instead using SecureRandom to get a cryptographically secure pseudo-random number generator for use by security-sensitive applications." source

Answer 2

Several others have mentioned reproducibility. Reproducibility is at the heart of debugging, you need to be able to reproduce the circumstances in which the bug occurred.

Another important use of reproducibility is that you can play some statistical games to reduce the variability of some estimates. See Wikipedia's Variance Reduction article for more details, but the intuition is as follows. Suppose you're considering two different layouts for a bank or a grocery store. You can't build them both and see which works better, so you use simulation. You know from queueing theory that the size of lines and delays customers experience are partly due to the layout, but also partly due to the variation in arrival times, demand loads, etc, so you use randomness in your two models. If you run the models completely independently, and find that the lines are bigger in layout 1 than in layout 2, it might be because of the layout or it might be because layout 1 just happened to get more customers or a more demanding mix of transactions due to the luck of the draw. However, if both systems use the exact same set of customers arriving at the same times and having the same transaction demands, it's a "fairer" comparison. The differences you observe are more likely to be because of the layout. You can accomplish this by reproducing the randomness in both systems - use the same seeds, and synchronize so that the same random numbers are used for the same purpose in both systems.

Answer 3

One use of this is that it enables you to reproduce the results of your program in future.

As an example, I wanted to compute a random variable for each row in a database. I wanted the program to be reproducible, but I wanted randomness between rows. To do this, I set the random number seed to the primary key of each row. That way, when I ran the program again, I got the same results, but between rows, the random variable was pseudo random.

Answer 4

I can see two reasons for doing this:

1. You can create a reproducible random stream. For a given seed, the same results will be returned from consecutive calls to (the same) nextX methods.

   If two instances of Random are created with the same seed, and the same sequence of method calls is made for each, they will generate and return identical sequences of numbers

2. You feel, for some reason, that your seed is of a higher quality than the default source (which I'm guessing is derived from the current time on your PC).

Answer 5

The seed is used to initialize the random number generator. A seed is used to set the starting point for generating a series of random numbers. The seed sets the generator to a random starting point. A unique seed returns a unique random number sequence.

This might be of help .

A pseudorandom number generator (PRNG), also known as a deterministic random bit generator DRBG, is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in that it is completely determined by a relatively small set of initial values, called the PRNG's state, which includes a truly random seed.