What is your favorite "best practice" when designing and implementing computational models? Mine is: As far as possible, isolate stochasticity.
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I don't think my view is orthodoxy at all though. It's essentially, "I'd rather constantly run stochastic-albeit-reproducible-on-failure tests to verify my expectations than occasionally run more expensive, fuller fuzzing tests." Mostly, it aligns with my dev habits well.
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It makes a lot of sense!
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