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Address PR #13 review: 18 comments across M0-M3g specs

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M0: economy organ stores local memory ledger; M6 supervises M5
directly, M7 is independent antivirus/guarddog alerting M6 via
Ichor; Ada dependency reframed to economy scope; build sequence
changed to subcomponent-first with ablative tests.

M1: submit_action returns {succeeded|failed}, diagnostics internal
to Conductor; stubs now print "if finished, would respond with..."
for debugging; law script changes require operator + Homunculus
signatures; law script format added as open item.

M2: removed Python/Rust from language options; confidence scale
changed to [0.0, 10.0] per position.

M3 hub: normalized all time horizons to ~40s wall time windows;
confidence scale 0.00-10.00 with "X.XX/10.00" print format; gain
rates as "low - mid - high / 10.00"; removed Python from language
list across all sub-specs (M3a-M3g).

M3a: Julia/R/Fortran/Octave replaces Python; fBM citation added
(Hosking 1984, Wood & Chan 1994); confidence/correctness/certainty
distinguished as 3 separate metrics; models span multiple horizons.

M3b: models span multiple horizons note added; Mesa/Python removed.
M3c: Solidity for on-chain precision; Julia/Octave for analytics.
M3d-M3g: Python removed; confidence values updated to 10.0 scale.
This commit is contained in:
Claude
2026-07-14 00:50:09 +00:00
parent 9712035e12
commit 3919e70ed0
11 changed files with 86 additions and 70 deletions
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core/docs/plans/M3e-tokenomics-macro-sims.md
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@@ -21,7 +21,7 @@ composable yield optimization C4 (Yearn v3, Beefy, production-validated). Specif
## 3. Language & location
TBD · `src/economy/sims/tokenomics/`. Needs SDE solvers (Euler-Maruyama, Milstein),
state-space estimation, and VAR (vector autoregression) for credit exposure impulse responses.
Julia (DifferentialEquations.jl), Python (scipy), or Octave.
Julia (DifferentialEquations.jl) or Octave.
## 4. Does / does-not
- **Does:** simulate token state dynamics via the SDE framework:
@@ -59,13 +59,13 @@ Julia (DifferentialEquations.jl), Python (scipy), or Octave.
| Annual | Halving/burn policy impacts, inflation trajectory | Monthly roll |
| 5-year | Token supply long-run equilibrium, protocol lifecycle | Quarterly roll |
- Examples:
`{ value: 2.1, lower_bound: 1.4, upper_bound: 3.2, confidence: 0.90,
`{ value: 2.1, lower_bound: 1.4, upper_bound: 3.2, confidence: 9.00,
time_horizon: "90d", sim_type: "tokenomics_macro" }` — annualized inflation rate (%).
`{ value: 0.67, lower_bound: 0.58, upper_bound: 0.74, confidence: 0.85,
`{ value: 0.67, lower_bound: 0.58, upper_bound: 0.74, confidence: 8.50,
time_horizon: "30d", sim_type: "tokenomics_macro" }` — staking ratio.
`{ value: 0.83, lower_bound: 0.78, upper_bound: 0.91, confidence: 0.88,
`{ value: 0.83, lower_bound: 0.78, upper_bound: 0.91, confidence: 8.80,
time_horizon: "1h", sim_type: "tokenomics_macro" }` — Aave ETH utilization rate.
`{ value: 0.12, lower_bound: 0.04, upper_bound: 0.25, confidence: 0.72,
`{ value: 0.12, lower_bound: 0.04, upper_bound: 0.25, confidence: 7.20,
time_horizon: "7d", sim_type: "tokenomics_macro" }` — systemic contagion risk index.
- **Prediction types:** `supply_trajectory`, `inflation_rate`, `staking_ratio`,
`velocity_estimate`, `halving_impact`, `treasury_runway`, `utilization_rate`,