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Acts on a correctness/completeness review of the countable uncle model and its report material. Correctness fixes in the report: - s3.4 quoted 0.998 for W_abs=10 at the 8s budget; the run says 0.9963. - s1 claimed both models >= 0.996 at U >= 1; countable U=2 delta=8 is 0.9955. Corrected to >= 0.995. - The s3.2 table presented two cells (U=1 at delta 16 and 32) as model differences. They are not resolvable: t = 0.46 and 0.47 over 5 replicates. The table now carries +-SEM and a t per cell. - s3.4 claimed the ~7-block-interval floor "carries over unchanged". Accuracy is still climbing past W=7 at every delay (8s: 0.989 -> 0.996), so the claim is dropped. The 32s curve is non-monotonic with replicate SD up to 0.22 and is now flagged as noise, not a trend. - 1-r was attributed to the first-fork restriction alone; it is the combined first-fork and capacity loss, which this measurement cannot separate. Hedged to match fig32's own axis label. Completeness: the U=0 negative control was swept but never reported. With no uncles the two models are identical by construction, yet they differ by -0.23 at delta_max=32 (t=2.1) because they draw independent RNG streams. That is the noise floor the rest of the grid must clear, and it is now in s3.2, s9, fig30 and the config header. New study (configs/fine-delay.yaml, scripts/plot_fine_delay.py, s3.2a, fig34/fig35): the design band delta_max 1-5 at 40 replicates, both models. Findings: every U >= 1 cell of both models lands in 0.998-1.001, flat in delay, while U=0 decays 0.810 -> 0.640. No individual cell resolves a model difference (widest 95% CI +-0.15pp; max t=2.59 vs Bonferroni 2.94 over 15 cells). Pooled across uncle caps the first-fork cost is monotone in delay and separates from zero only at delta_max=5 (-0.0014 +- 0.0007, t=3.7) -- below 0.15% everywhere in the band, against +-0.9% per-epoch sampling noise. Code: - deep_ref_share is identically 0 on every real countable run: for a chain block B the producer's chain below B is the counting chain below B, so the counting-side parent-on-chain re-check cannot reject what selection emitted. It is a drift alarm, not a rate. Documented as such in measure.py, the plot docstring and the config header, and pinned by a new end-to-end test. - Removed annotate_uncles: a second countable implementation that production never called, while carrying most of the selection test coverage. Tests now drive select_uncles_at_production through an annotate_via_production replay helper -- same assertions, live path. - Added tests for the two previously uncovered branches of the live selection: the pmin/below chain walk that resolves parent-on-chain for candidates whose parent sits below the window, and the occupied-slot exclusion built from the chain walk. - theory.q_effective and theory.window_miss_prob were unused and untested. Now used (the prediction figure reconstructs q_u through the identity the report quotes) and tested. The window_miss_prob test records that its "~ e^-W" docstring is the f->0 limit: the true decay is e^-1.017W at f=1/30, 16% off by W=10. - Shared sem()/recovery_rate() moved into figures_pernode.py; fig30 and fig33 regenerated with SEM error bars and the U=0 control curve. - Fixed the pre-existing E501 in bootstrap_dynamics.py; ruff clean. Report prose reworked to read standalone: the countable model is described as the rules under analysis and the former model as a labelled "unrestricted" comparison baseline, with no dated banners and no round-to-round narration. Tests: 209 passed (was 202). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Total-Stake-Inference parameter selection
Per-node network simulation of Cryptarchia Total Stake Inference (TSI). Simulator: tsi-sim-pernode. All runs at the true security parameter k = 2160 unless noted; latency is in slots and 1 slot = 1 s.
This report selects and justifies the TSI parameters for Cryptarchia from a per-node network simulation. It is split into four cohesive parts; section numbers (§1–§9, A–C) are stable identifiers preserved across the set.
Uncle references. The model analysed throughout is the countable one: counting-only references, deduplicated by slot, drawn from first-fork blocks only, within a window derived as
w_u = W_abs/f. An unrestricted baseline — any orphan in the window at any fork depth — is measured alongside it for comparison. The two are indistinguishable in the design regimeρ < 1and diverge only under overload. See the model note at the top of Part 1, the mechanism in §2.1, the comparison in §3.2–§3.2a, and the reproduction notes in §9.
Parts
- Overview and recommendations — the executive summary, the per-knob parameter reference (§7), and the safest selection with residual risks and the recommendation-vs-spec deltas (§8).
- Accuracy and design — the model and counting rule (§2), the seven findings and their evidence (§3), the design equations and selection algorithm (§4), and the caveats and regime of validity (§5).
- Robustness and incentives — jitter, grinding, withholding, selfish mining, the reward design, fork/reorg depth, and organic churn (§6).
- Reproducibility and appendices — how to re-run every study (§9), the residual f-rounding offset (App A), the per-epoch noise floor (App B), and consensus detail (App C).
Headline recommendation
Cryptarchia baseline f = 1/30. Two design choices are foundational: count uncles per occupied slot, not per block — the density-bug fix that lands the estimate at exactly D (§2.1, §8.5) — and make genesis D̂ a single protocol constant, identical at every node, never client-configurable, since a per-node divergence is never self-corrected (§8.1 row 7). The settings: security k = 2160, uncle window W = 300 slots, uncle cap U ≥ ⌈ρ⌉ + 1 (2 at the Blend target; the protocol's MAX_UNCLES = 4 sits safely above it), learning rate β = 1, on-chain f at 10⁻⁶ precision, peering degree ≥ 6 at scale, soft uncle rewards with w_u + w_n < 1, and operate at load ρ = f·D_vis < 1. The full recommended-configuration table and rationale are in Part 1 →.
Figures
Figures are embedded from report-figures/ via relative links and are versioned here alongside the report. They are produced by the simulator's plotting scripts (scripts/*.py and tsi_sim.plotting) in tsi-sim-pernode; that simulation folder does not commit its own generated figures — the copies checked in here are the report's figures of record.
Reproducing the results
The simulation code, configs, and run data live in tools/simulators/tsi/tsi-sim-pernode. Every study's exact command is listed in Part 4 — Reproducibility (§9). In short, from the simulator directory: make install, then make <config> to run a sweep (results land under runs/<timestamp>_<label>/), and the per-figure generators under scripts/ render the figures. Regenerated figures must be copied into report-figures/ to update this report.