Analyses based on iterative model fitting or simulations are typically seeded with an unpredictable random value based on a PRNG function plus the program / computer state. This causes analyses to return different results each time, unless the user remembers to hard-code a random seed into their analysis. It can lead to problems when comparing different runs, reproducing analyses that have been reported in publications, or dealing with potential for p-hacking in cases of borderline significance.
Since statistical analyses are meant to be precisely replicable, why isn't the default behavior to use a deterministic seed? For example, a seed could be based on a hash of the dataset or the regularlized command input. This would cause replications (identical analysis with identical data in the same software) to return reliable results, unless the user intentionally specifies a different seed.
The current default does ensure that if a seed leads to pathological model results (e.g., a terrible local maximum), the next run won't have the same problem. But statisticians usually shouldn't be re-running the same command to see whether it turns out differently anyway (outside of methods like bootstrapping, where ensembling is built in to the assumptions and the analysis). Is there another reason for using unpredictable and unrecorded random seeds?