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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/M3d-mev-adversarial-sims.md
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@@ -19,9 +19,9 @@ optimization C3 (emerging — SMFRL solvers); Kolokoltsov adversarial C3 (non-li
WENO discretization established but crypto application novel). Parameterization C1.
## 3. Language & location
TBD · `src/economy/sims/mev/`. Needs combinatorial optimization (PuLP/OR-Tools for knapsack),
TBD · `src/economy/sims/mev/`. Needs combinatorial optimization (OR-Tools for knapsack),
continuous-time auction modeling, PDE solvers (WENO for shock-capturing in adversarial dynamics),
and bilevel optimization (DSMFG). Python, Rust, or Julia.
and bilevel optimization (DSMFG). Julia, Fortran, or C++.
## 4. Does / does-not
- **Does:** simulate Priority Gas Auctions where multiple searcher bots compete for the same
@@ -57,11 +57,11 @@ and bilevel optimization (DSMFG). Python, Rust, or Julia.
| Annual | Kolokoltsov adversarial long-run dynamics | Monthly roll |
| 5-year | Structural MEV regime shifts, protocol-level policy effects | Quarterly roll |
- Examples:
`{ value: 0.23, lower_bound: 0.11, upper_bound: 0.38, confidence: 0.80,
`{ value: 0.23, lower_bound: 0.11, upper_bound: 0.38, confidence: 8.00,
time_horizon: "next_block", sim_type: "mev_adversarial" }` — sandwich probability.
`{ value: 14.7, lower_bound: 8.2, upper_bound: 22.5, confidence: 0.75,
`{ value: 14.7, lower_bound: 8.2, upper_bound: 22.5, confidence: 7.50,
time_horizon: "next_block", sim_type: "mev_adversarial" }` — optimal gas bid (gwei).
`{ value: 0.034, lower_bound: 0.018, upper_bound: 0.052, confidence: 0.82,
`{ value: 0.034, lower_bound: 0.018, upper_bound: 0.052, confidence: 8.20,
time_horizon: "1h", sim_type: "mev_adversarial" }` — cross-chain arb profit (ETH).
- **Prediction types:** `sandwich_probability`, `frontrun_risk`, `optimal_gas_bid`,
`block_inclusion_probability`, `mev_exposure`, `cross_chain_arb_profit`,