In the R programming language, the random number generator (RNG) is seeded each session using the current time and process ID. Via the magic of the popular Mersenne Twister PRNG, the values stored in .Random.seed are used sequentially each time "randomness" is invoked in a function. This means, of course, that the same function run in different R sessions can produce varying results, and in the case of modelling a system sensitive to initial conditions the observed differences could be huge.