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Resolve economy organ toolchains: ECLiPSe 7.2 + COIN-OR, M3g to Fortran, BoundedPrediction fields

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Hook:
- ECLiPSe upgraded 7.1_13 → 7.2_13, adds ic + eplex (if_osiclpcbc) with
  COIN-OR system dep, sha256 pinned, ECLIPSEDIR exported, correct paths
  (lib/x86_64_linux/eclipse.exe not bin/)
- fpm switched from GitHub binary download to pip (0.12.0) — proxy blocks
  GitHub release downloads in this environment
- Alire download gets sha256 verification on both install and presence check
- Fortran comment updated M3d,M3e → M3d,M3e,M3g
- Foundry section comment clarified as hosted separately

Specs:
- M3g §3: Zig → Fortran 2018 (gfortran/fpm/OpenBLAS, hand-rolled Riccati)
- M3 hub §3: sub-process list Zig(M3g) → Fortran(M3g)
- M3 hub §5: BoundedPrediction adds token_ticker and recent_shift (ground
  truth from M2, same source as correctness scoring and calibration)
- M3b §3: ECLiPSe 7.2 + ic + eplex (COIN-OR CLP/CBC)
- M3f §3: ECLiPSe 7.2 + ic + eplex (COIN-OR CLP/CBC)
- M3c §3: Solidity + Foundry confirmed, hosted separately, same Hub path
This commit is contained in:
Claude
2026-07-18 03:55:43 +00:00
parent 700a1825bf
commit e408409b77
6 changed files with 77 additions and 45 deletions
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core/docs/plans/M3-sims-hub.md
+10 -5
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@@ -18,8 +18,8 @@ established); specific model parameters C1.
## 3. Language & location
**Tcl** · `src/economy/sims/`. The hub is a syntax-agnostic coordinator: Tcl manages
lifecycle, tick-advancement, and query routing for sub-sims in their native runtimes via
stdin/stdout JSON — **Fortran** (M3d, M3e), **Prolog** (M3b, M3f), **R** (M3a),
**Solidity** (M3c), **Zig** (M3g). Tcl imposes no type system or paradigm on the
stdin/stdout JSON — **Fortran** (M3d, M3e, M3g), **Prolog** (M3b, M3f), **R** (M3a),
**Solidity** (M3c). Tcl imposes no type system or paradigm on the
sub-processes it orchestrates.
## 4. Does / does-not
@@ -50,17 +50,22 @@ sub-processes it orchestrates.
`SimType` ∈ { `statistical`, `sociological`, `amm_liquidity`, `mev_adversarial`,
`tokenomics_macro`, `consensus_staking`, `market_microstructure` } (M3a–M3g).
- `BoundedPrediction { value, lower_bound, upper_bound, confidence, correctness, certainty,
time_horizon, sim_type, timestamp }`.
time_horizon, sim_type, timestamp, token_ticker, recent_shift }`.
Every output is bounded — no point estimates without uncertainty ranges.
Three quality metrics, each ∈ [0.00, 10.00]:
**confidence** — how sure the model is of this prediction;
**correctness** — how accurate the model has been historically;
**correctness** — how accurate the model has been historically (scored against literal
market values from M2);
**certainty** — how stable the estimate is across perturbations.
**token_ticker** — which asset this prediction concerns (e.g. `"ETH"`, `"BTC"`).
**recent_shift** — literal observed market movement (ground truth from M2, not sim output).
Same M2 source feeds calibration and `correctness` scoring.
Gain rates print as `lower - value - upper / 10.00`
(e.g. `2.31 - 4.44 - 7.11 / 10.00 gain over next 30 days`);
the denominator aids legibility — gain is not capped at 10.00.
Example: `{ value: 7.2, lower_bound: 5.8, upper_bound: 8.9, confidence: 7.30,
correctness: 8.10, certainty: 6.50, time_horizon: "4h", sim_type: "amm_liquidity" }`.
correctness: 8.10, certainty: 6.50, time_horizon: "4h", sim_type: "amm_liquidity",
token_ticker: "ETH", recent_shift: -0.023 }`.
- `status(sim_type?) -> { running, pop_count, last_calibration, data_freshness }` — health check.
- `calibrate(sim_type, feed_data: [NormalizedDatum])` — Data Feeds (M2) pushes live data for
model recalibration.