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The simulator gitignores its runs/ directory, so every table and figure in reports/blend/pd rested on data that existed only on one machine. This adds the sweep outputs of record under reports/blend/pd/data -- one directory per study, 1 MB total -- so any number can be checked against its source, or challenged, without re-running hours of compute. report_numbers.py comes with them: run it and it prints every value the report quotes together with its across-topology standard error, straight from these parquets. It reproduces the report tables exactly. Kept: default (8000 rounds/cell), redundancy (9600), percolation (6400), correlated-churn (6400), fullscale (192, the deliberately lighter 1e6 check). Omitted: the smoke runs, and an earlier 144-rounds/cell redundancy grid whose sampling error produced a non-monotonic delivery curve -- superseded, and the reason the kept grid samples 9600. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
44 lines
2.6 KiB
Markdown
44 lines
2.6 KiB
Markdown
# Evidence of record
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The sweep outputs behind every number in [the report](../README.md). The simulator does not commit
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its own `runs/` directory — these are the copies of record, kept so that any figure or table can be
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re-derived, or challenged, without re-running hours of compute.
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Each run directory holds the three tables the simulator writes: `propagation.parquet`,
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`adversary.parquet` and `deanon.parquet`.
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| directory | config | sampling | backs |
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|---|---|---|---|
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| `default/` | `configs/default.yaml` | 1 000 rounds × 8 seeds = **8 000/cell** | §3.1–§3.5 — delay, observation, eclipse, deanonymization, delivery, coverage |
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| `redundancy/` | `configs/redundancy.yaml` | 1 200 × 8 = **9 600/cell** | §3.8 — messaging redundancy R = 1…4 |
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| `percolation/` | `configs/percolation.yaml` | 800 × 8 = **6 400/cell** | §3.5 — the churn threshold `u_c = 1 − 1/(degree − 1)` |
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| `correlated-churn/` | `configs/correlated-churn.yaml` | 800 × 8 = **6 400/cell** | §3.9 — correlated AS/region outages vs uniform churn |
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| `fullscale/` | `configs/fullscale.yaml` | 64 × 3 = **192/cell** | §5 — the 10⁶ scaling check (deliberately lighter; not a source of headline numbers) |
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The linkability results (§3.6–§3.7) and both deanonymization rates are closed forms over these
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tables rather than separate measurements, so they have no run of their own — `pd.linkability`
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derives them and `make verify` checks them against Monte-Carlo.
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## Regenerating the report's numbers
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```
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python report_numbers.py
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```
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prints every quoted value with its across-topology standard error, straight from the parquets here.
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That is the fastest way to check a table in the report against its evidence. It takes optional
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paths (`report_numbers.py <default> <redundancy> <percolation>`) if you want to point it at fresh
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runs instead.
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## Regenerating the data itself
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From [`tools/simulators/blend/pd`](../../../../tools/simulators/blend/pd): `make sweep`,
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`make redundancy`, `make percolation`, `make correlated-churn`, `make sweep-fullscale`. Results
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land in that simulator's `runs/<timestamp>_<label>/`. Note that the seed streams depend on the
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configuration, so re-running reproduces the *statistics*, not bit-identical numbers, unless the
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config is unchanged — in which case it does reproduce exactly.
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Two runs from the same session are deliberately **not** kept: the smoke runs (throwaway, far too
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noisy to interpret) and an earlier 144-rounds/cell redundancy grid that was superseded because its
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sampling error produced a non-monotonic delivery curve — the reason `redundancy/` samples 9 600.
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