Philosophy
Classification Without Sisyphean Taxonomies: Bisimulation as ‘Distinguishable by Experiment’
“The Pledge, Part IV.” — Lucius Meredith (Feb 2026)
In brief: Classification by naming tends to expand without converging on stable, testable boundaries. Bisimulation provides a formal model for grouping behaviors into equivalence classes defined by what observations can separate them. This framing emphasizes reproducibility: classification improves when experiments are explicit and repeatable.
Key takeaways
- Classification by naming tends to expand without converging on stable, testable boundaries.
- Bisimulation provides a formal model for grouping behaviors into equivalence classes defined by what observations can separate them.
- This framing emphasizes reproducibility: classification improves when experiments are explicit and repeatable.
Key questions
- How do you choose which experiments define the classification?
- What happens when the classification granularity doesn’t match the use case?
- Where does bisimulation-based classification offer advantages over statistical clustering?
Why it matters for developers
A behavior-first approach pushes you to define explicit probes and keep evidence of outcomes, so you can audit why something was classified the way it was.
classification
bisimulation
equivalence classes
reproducibility
experimental method