run-mental-experiment

To convert a thought experiment into an executable simulation, you formalize abstract hypotheses into Python scripts with configurable parameters. This process validates invariants and produces auditable results for evidence-based decision-making.

You validate software algorithm performance by running simulations with configurable parameters and fidelity levels. This tests how algorithms handle specific conditions like large datasets before actual implementation.

Yes, you need Python to run hypothesis testing simulations. Python is required for executing the simulation scripts, while various other tools may be needed based on the specific domain.

Yes, you can create regression tests for software failure modes and boundary conditions. The simulation framework formalizes these scenarios to test invariants and ensure reproducible, auditable results.

The best way to ensure simulation reproducibility is using a scientific reporting protocol with configurable parameters. This formalizes thought experiments into auditable simulations that validate hypotheses.

Yes, run-mental-experiment supports configurable fidelity levels for simulations. It converts thought experiments into executable scripts to validate hypotheses and test invariants across various simulation tools.