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The study started as a peering-degree question and grew well past it: propagation, adversary exposure, deanonymization and time-to-link, reliability under uniform and correlated churn, messaging redundancy, and cover traffic. The pd name no longer describes it. tools/simulators/blend/pd/ -> tools/simulators/blend/, package src/pd -> src/blend, and reports/blend/pd/ -> reports/blend/. Moved with git mv so history follows. The text substitutions are deliberately narrow. pd is also the conventional pandas alias, and pandas genuinely has a pd.plotting submodule, so a blanket pd. -> blend. rewrite would have corrupted four files. Only package-unambiguous forms were changed: from pd.X, -m pd.X, pd.<our module>, PD_BYTES_BUDGET, src/pd, and the pyproject name. All four import pandas as pd lines are untouched and verified. Both READMEs reframed: peering degree is now presented as the primary axis that ties the others together rather than as the subject, and the relative links, which lost a directory level in the move, are corrected. Verified after the move: ruff clean, 101 tests, 45 verify anchors, make targets, the script shims, an end-to-end smoke run, and data/report_numbers.py still reproducing the report tables from the checked-in evidence. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
24 lines
1.0 KiB
YAML
24 lines
1.0 KiB
YAML
# Messaging-redundancy study: R independent blend cascades per emission (R in 1..4).
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# Measures delivery_R (reliability) and deanon_R / full_deanon_R (anonymity cost), and feeds the
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# time-to-link / stake-inference figures.
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#
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# Statistics: 1200 rounds x 8 topology seeds = 9600 rounds per cell -> binomial SEM <= 0.005 on
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# every delivery rate, enough to resolve the 1-(1-p)^R law (a coarser grid was non-monotonic in R).
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# degree 3 is included because it is the one setting where the responsive sub-graph fragments, so
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# it tests whether redundancy can buy back coverage (it cannot -- see the report).
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# max_blend_delay=0 keeps it affordable: redundancy multiplies propagation cost by R, and mixing
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# delays are not the subject here (the headline delay study is configs/default.yaml).
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n_nodes: [20000]
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degree: [3, 8]
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blend_hops: [1, 3]
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max_blend_delay: [0]
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unresponsive_frac: [0.0, 0.1, 0.2, 0.3, 0.5]
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redundancy: [1, 2, 3, 4]
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f_adv: [0.1, 0.2, 0.33]
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adversary_mode: [random, worstcase_coverage]
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seeds: 8
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base:
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n_rounds: 1200
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n_placements: 4
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worstcase_max_n: 100000
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