58.8 Technical Details - Random Number Generator
Monte Carlo simulations in TreeAge Pro make use of a robust pseudo-random number generator (RNG) algorithm in Java, which has the following useful properties:
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Has negligible serial correlation between successive values in the output sequence.
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Works fast.
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Passes numerous tests for statistical randomness.
Distribution sampling and discrete simulation random walks utilize the RNG. By default, each RNG is “seeded” using the computer clock. Users can override this “random” seed and specify a starting seed value. This results in subsequent simulations generating identical results (provided there were no changes to the model). The seeding option can be found in Tree Preferences > Analysis Settings > Monte Carlo Options > Random Number Seeding Options.
In a multi-processor simulation, each thread has a separate RNG, which is started at a different position based on the clock seed or based the user-specified seed value.
More information about seeding options can be found in Tree Preferences section Monte Carlo Options
