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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
114 lines
7.0 KiB
Markdown
114 lines
7.0 KiB
Markdown
# M3 — Sims hub (market prediction simulations)
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## 1. Component
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The economy organ's prediction engine: **always-running simulations** ("Sims") populated by
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autonomous simulation agents ("Pops") that model market dynamics across multiple mathematical
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domains and time scales. Sims produce raw simulation data; the hub transforms it into
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**predictions with explicit upper and lower bounds** and publishes them continuously to the
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Marketplace via M2 Data Feeds. Traders query predictions from the Marketplace (M1), not from the
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hub directly. This is the hub spec; individual sim types have dedicated sub-specs (M3a–M3g).
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The academic foundations span AMM mechanism design [1,2], MEV game theory [3,4,5], macro
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tokenomics via SDEs [6,7], and evolutionary consensus games [8–11].
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## 2. Status / certainty
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DESIGN-FIRST · ABSENT. Role C3; implementation C1. Mathematical foundations C4 (literature
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established); specific model parameters C1.
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## 3. Language & location
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**Tcl** · `src/economy/sims/`. The hub is a syntax-agnostic coordinator: Tcl manages
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lifecycle, tick-advancement, and query routing for sub-sims in their native runtimes via
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stdin/stdout JSON — **Fortran** (M3d, M3e, M3g), **Prolog** (M3b, M3f), **R** (M3a),
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**Solidity** (M3c). Tcl imposes no type system or paradigm on the
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sub-processes it orchestrates.
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## 4. Does / does-not
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- **Does:** tick-advance continuously at **90:1** (90 simulated seconds = 1 wall-second)
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across **six concurrent time horizons** — tick/hourly, daily, weekly, monthly, annual, and
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5-year forecast windows; every tick advances every sim; maintain populations of Pops whose
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behaviors emerge from the sim's mathematical model; ingest live data from Data Feeds (M2)
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for calibration; transform raw sim data into bounded predictions and publish them continuously
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to the Marketplace via M2; produce outputs with **explicit upper/lower bounds** on every
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prediction value.
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| Horizon | Window | Tick step | Effective ratio | Wall time for window |
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|---------|--------|-----------|-----------------|---------------------|
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| Tick–hourly | Next 1–60 min | 1s | 90:1 | ~40s |
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| Daily | Next 24h | 24s | 2,160:1 | ~40s |
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| Weekly | Next 7d | ~3 min | 15,120:1 | ~40s |
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| Monthly | Next 30d | 12 min | 64,800:1 | ~40s |
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| Annual | Next 365d | ~2.5 hr | ~788,000:1 | ~40s |
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| 5-year | Next 1825d | 12 hr | ~3,942,000:1 | ~40s |
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- **Does-not:** trade (Traders/Marketplace do); make decisions for traders (it informs, they
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decide); enforce laws (Marketplace does); supervise behavior (Conductor/SAE do); receive
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trader queries (traders query the Marketplace); skip ticks; run slower than 90:1.
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## 5. Interface contract
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- `publish(sim_type: SimType, prediction: BoundedPrediction)` — the hub continuously transforms
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raw sim data into predictions and publishes them to the Marketplace via M2 Data Feeds. This is
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a constant stream, not on-demand. Traders query predictions from the Marketplace (M1), not from
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the sim hub.
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`SimType` ∈ { `statistical`, `sociological`, `amm_liquidity`, `mev_adversarial`,
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`tokenomics_macro`, `consensus_staking`, `market_microstructure` } (M3a–M3g).
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- `BoundedPrediction { value, lower_bound, upper_bound, confidence, correctness, certainty,
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time_horizon, sim_type, timestamp, token_ticker, recent_shift }`.
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Every output is bounded — no point estimates without uncertainty ranges.
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Three quality metrics, each ∈ [0.00, 10.00]:
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**confidence** — how sure the model is of this prediction;
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**correctness** — how accurate the model has been historically (scored against literal
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market values from M2);
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**certainty** — how stable the estimate is across perturbations.
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**token_ticker** — which asset this prediction concerns (e.g. `"ETH"`, `"BTC"`).
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**recent_shift** — literal observed market movement (ground truth from M2, not sim output).
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Same M2 source feeds calibration and `correctness` scoring.
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Gain rates print as `lower - value - upper / 10.00`
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(e.g. `2.31 - 4.44 - 7.11 / 10.00 gain over next 30 days`);
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the denominator aids legibility — gain is not capped at 10.00.
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Example: `{ value: 7.2, lower_bound: 5.8, upper_bound: 8.9, confidence: 7.30,
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correctness: 8.10, certainty: 6.50, time_horizon: "4h", sim_type: "amm_liquidity",
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token_ticker: "ETH", recent_shift: -0.023 }`.
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- `status(sim_type?) -> { running, pop_count, last_calibration, data_freshness }` — health check.
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- `calibrate(sim_type, feed_data: [NormalizedDatum])` — Data Feeds (M2) pushes live data for
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model recalibration.
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## 6. Dependencies & stubs
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- M2 Data Feeds — calibration data source; *stub:* canned market data.
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- M1 Marketplace — prediction consumer (via M2); *stub:* print predictions.
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- M3a–M3g sub-specs — individual sim implementations; *stub:* each returns fixed predictions.
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## 7. Invariants / laws
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- **L1 (C5):** sims are **always running** — they are not invoked on demand. Traders query
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current state; they don't trigger computation.
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- **L2 (C5):** every prediction output includes **explicit upper and lower bounds** — no
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unbounded point estimates. Uncertainty is a first-class value, not an afterthought.
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- **L3 (C5):** all sims are **tick-advanced and continuous** — fine-grained ticks (RTS-style).
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Base speed **90:1** (1s wall = 90s sim). Longer horizons run at higher velocity with coarser
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steps and update less frequently. Each horizon completes its forecast window in **~40s wall
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time**. Each horizon runs **in parallel** — they are concurrent, not sequential. No horizon
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runs slower than 90:1.
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- **L4 (C4):** sims are **read-only from traders' perspective** — traders consume predictions
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from the Marketplace; they cannot mutate sim state. Calibration happens only from Data Feeds
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(M2).
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- **L5 (C4):** each sim type is **independent** — failure in one sim does not cascade to others.
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Degraded sims report their status; traders handle missing predictions.
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- **L6 (C3):** Pops are **simulation constructs, not AI actors** — they follow mathematical
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rules within the sim. Traders (M5) are the AI actors.
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## 8. Build steps
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1. Define `BoundedPrediction` shape (value, bounds, confidence/correctness/certainty).
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2. Build the sim runner (lifecycle management for always-on sims).
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3. Wire M2 Data Feeds → calibration pipeline.
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4. Implement sub-specs M3a–M3g as they land.
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5. Wire continuous prediction publishing → M2 → Marketplace.
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## 9. Tests
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Always-on: sim running after init without external trigger. Bounded output: every prediction has
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lower ≤ value ≤ upper. Three metrics: confidence, correctness, certainty all present in every
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output. Publishing: predictions flow continuously to Marketplace via M2. Independence: one sim's
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failure doesn't affect others. Calibration: new data updates model state.
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## 10. Open items
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- Pop lifecycle (birth/death/mutation within sims, or fixed populations?).
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- Cross-sim aggregation (do traders query individual sims, or is there a meta-prediction layer?).
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- Calibration frequency per sim type.
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- Computational budget per sim (how much CPU/GPU each can consume).
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