Field notes / Experiment guide
Which parts of an ant’s world keep it close to home?
Some ants repeatedly return to a small home region. Is that behaviour stored in particular patches of the colour pattern they have written?
New to these ants? Start here.
An ant moves on a grid of coloured squares. Each colour tells it to turn left (L) or right (R). It advances that square to the next colour, then steps forward. A rule such as RLR is the list of turns, starting at colour zero. On a blank grid the same rule always makes the same trajectory.
Territory means every cell visited so far. Its boundary consists of visited cells with an unvisited neighbour. A highway is a repeating pattern of motion that carries the ant away. A tether means repeated returns to a small home region.
Growing like the cube root of time means taking eight times as many steps to double a linear size. The rotor walker is a comparison model that follows and rotates arrows stored on cells. A seed makes a random starting pattern reproducible; the ant itself can still move deterministically.
We distinguish c, the chance of discovering a cell per boundary step, from kappa (κ), time at the boundary normalized by perimeter divided by area. Explaining their values is harder than using them to account for growth.
A closer look
The idea under test
If a patch carries the tether, scrambling it should reduce returns home more than changing a comparable random patch.
What we learned
The knockout maps identify small sensitive regions in the two hard tethered rules. Later experiments investigate their colours, age and individual cells.
What this leaves open
Sensitivity identifies where an intervention matters; it does not explain how the intact pattern guides a return.
Read the evidence and reproduction recipe ↗
Inside the experiment
Methods, registered predictions, exact commands and saved analysis. These are the working research documents; expand them for the detail behind the introduction.
Methods and original experiment record
knockout-maps
Migrated legacy investigation (2026-10-14). Its files used to sit in the flat data/ directory (and calib/, occ/, snaps/, dumps*, knock* where listed) with the label prefix below; experiments/_migration/map.tsv gives the exact old -> new path of every file. This stub was generated from file names and script docstrings. It is not the original write-up: the question, timestamped hypotheses, commands and results are in notes/ (mainly notes/frontier-results.md, notes/frontier-status.md, notes/research-frontier*.md; search for the label), and the producing command is usually in the script docstring or a run_*.sh here.
Old labels / prefixes: RLLLLRRRRLR, RRRRRLLLLLL, classko, followup, knock, knockout, verdict
Scripts
scripts/classko_stats.py: Item 14: class knockouts (colour classes, parity classes) vs matched random controls.scripts/followup_stats.py: Numbers for the page's follow-up section on the two open questions.scripts/knockout.py: Knockout map: shuffle one 5x5 patch of the real territory at a time.scripts/knockout_analyse.py: Analyse the coarse knockout map (see notes/knockout-map-hypotheses.md).scripts/knockout_checks.py: Ad hoc checks quoted in notes/knockout-map-results.md (run in WSL from the repo root, after knockout.py and knockout_analyse.py). Three sections: null hot-rate,scripts/knockout_followup.py: Checks after the coarse/refine maps (notes/knockout-map-results.md, 'Follow-up').
Results
20 entries in results/ (0 are .log/.done markers); csv/json/txt outputs, *_stats.txt hold the summaries quoted in the notes.
Bulk output (gitignored)
dumps/knock/
Notebook
No committed Jupyter notebook is available for this experiment yet.