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98a6f9a0b1
The initial M-series specs were wrong (text digestion pipeline). Replaced
with the actual economy organ architecture:
M0 hub (independent system, scoped autonomy, multi-layered braking)
M1 Marketplace (multi-trader harness, deterministic law script, veto)
M2 Data Feeds (RSS + live market, bridges Marketplace ↔ Sims)
M3 Sims hub + 7 sub-specs (always-running, bounded predictions):
M3a statistical, M3b sociological, M3c AMM/liquidity,
M3d MEV/adversarial, M3e tokenomics/macro, M3f consensus/staking,
M3g market microstructure
M4 Wallets (sovereign custody, our keys only, 1:1 trader binding)
M5 Traders (AI actors, wallet-bound, all tool calls monitored)
M6 Conductor (supervisory AI, veto, pause/investigate, SAE intake)
M7 SAE monitor (trader surveillance, Brain-compatible message format)
Grounded in AMM invariant mechanics, MEV game theory, SDE tokenomics,
and evolutionary consensus games. Tax stub for Verschwörern Veregeister.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
60 lines
3.1 KiB
Markdown
60 lines
3.1 KiB
Markdown
# M3a — Statistical & quantitative sims
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## 1. Component
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Pure statistical simulation: **Monte Carlo methods, Bayesian inference, time-series forecasting,
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and volatility modeling**. The mathematical backbone — no game theory, no sociology, just the
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numbers. Operates across multiple time scales (tick to weekly). Pops in this sim represent
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**stochastic sample paths**, not behavioral agents.
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## 2. Status / certainty
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DESIGN-FIRST · ABSENT. Mathematical foundations C4 (standard quant methods); parameterization C1.
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## 3. Language & location
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TBD · `src/economy/sims/statistical/`. Python (NumPy/SciPy), Julia, or R for numerical
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computing. Needs efficient matrix operations and distribution sampling.
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## 4. Does / does-not
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- **Does:** run Monte Carlo price simulations (geometric Brownian motion, jump-diffusion);
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Bayesian parameter estimation from live data (M2); time-series forecasting (ARIMA, GARCH for
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volatility clustering); Value-at-Risk and Expected Shortfall calculations; produce bounded
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predictions with confidence intervals as upper/lower bounds.
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- **Does-not:** model human behavior (M3b does); model protocol mechanics (M3c–M3f do);
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trade or recommend (Traders do).
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## 5. Interface contract
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- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
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- **Output bounds:** statistical confidence intervals.
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Example: `{ value: 1847.30, lower_bound: 1790.15, upper_bound: 1905.60, confidence: 0.95,
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time_horizon: "24h", sim_type: "statistical" }` — 95% CI on ETH price.
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- **Prediction types:** `price_forecast`, `volatility_estimate`, `var_calculation`,
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`correlation_matrix`, `regime_detection`.
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- Calibration: ingests `price_tick` and `dex_pool_state` from M2 Data Feeds.
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## 6. Dependencies & stubs
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- M2 Data Feeds — price history for calibration; *stub:* canned price series.
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- M3 Sims hub — lifecycle management; *stub:* manual init.
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## 7. Invariants / laws
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- **L1 (C4):** bounds are **statistical confidence intervals** — derived from the model's
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distribution, not hand-picked. The confidence level (e.g. 0.95) is explicit in the output.
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- **L2 (C4):** **multiple time scales run concurrently** — a tick-level volatility estimate and a
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weekly price forecast coexist; neither blocks the other.
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- **L3 (C3):** model parameters are **re-estimated on each calibration** from live data — no
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stale parameters carried across regime changes.
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## 8. Build steps
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1. Implement geometric Brownian motion Monte Carlo (simplest price sim).
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2. Add GARCH volatility estimation.
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3. Wire M2 price data → Bayesian parameter re-estimation.
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4. Implement the `BoundedPrediction` output with CIs.
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## 9. Tests
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Monte Carlo: N sample paths produce a distribution with correct mean/variance. CI: 95% interval
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contains true value ≥ 95% of the time on historical backtest. GARCH: volatility clusters detected
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in synthetic data. Calibration: new data shifts parameter estimates.
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## 10. Open items
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- Which distributions beyond GBM (heavy-tailed? Lévy?).
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- Regime-switching model complexity (hidden Markov? threshold?).
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- Computational budget (how many Monte Carlo paths per tick?).
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