Seeding and Repeatability: Reproducible Science Without Sacrificing Randomness

Seeding fixes the starting point of a pseudo‑random number generator (PRNG), making results repeatable. Use fixed seeds when you need comparability; use unpredictable seeds when you need uniqueness.

Why Seeds Matter

• Reproducibility: Same code + same seed = same sequence → same figures and tests.
• Debuggability: Deterministic runs simplify bug hunting and baseline comparisons.
• Collaboration: Share seeds and PRNG details so peers can rerun exactly.

Choosing a Seed Strategy

• Fixed seed for research/tests: Document seed, PRNG algorithm, and version. Consider separate seeds per module to avoid cross‑talk.
• Unique seed for production runs: Combine time with device/OS entropy; or rely on library defaults that source secure randomness.
• Security tasks: Skip PRNG seeding; use a CSPRNG provided by the OS/crypto library.

Practical Patterns

• Create a seed registry (per project) with named streams: seed_data, seed_model, seed_plot.
• Use independent streams or jumpable PRNGs for parallel jobs.
• Save seeds alongside outputs and configs for full provenance.

Pitfalls and Notes

• Collisions: Different seeds can produce overlapping subsequences in weak PRNGs. Prefer modern generators (e.g., PCG, xoshiro) with strong statistical properties.
• Global state leaks: Passing a seeded global RNG across modules can create subtle coupling; prefer explicit RNG instances.

FAQs

Can two seeds collide? With robust PRNGs, distinct seeds won’t produce identical streams, though subsets can look similar by chance. Use high‑quality algorithms and independent streams to mitigate.