mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-07-31 16:16:26 +00:00
1da2fa19c6
- M3 hub: add L3 invariant (360:1 minimum sim speed), six-horizon time table - M3a: add Heston stochastic vol, rough volatility (fBM), HMM regime detection, DCC-GARCH copula, jump-diffusion; six-horizon mapping - M3b: add Hegselmann-Krause bounded confidence, complex contagion, bandit- replicator hybrid, MFG (HJB+FP), pump-and-dump 3-type ABM; six-horizon mapping - M3c: add six-horizon time table - M3d: add Kolokoltsov adversarial (non-linear FP + WENO), DSMFG bilevel optimization, cross-chain adversarial arbitrage; six-horizon mapping - M3e: add kinked lending rate model, DeXposure inter-protocol credit network, composable yield optimizer; six-horizon mapping - M3f: add MFG for validator populations, six-horizon time table - M3g: add Almgren-Chriss optimal execution, six-horizon mapping - CLAUDE.md: add subagent productivity note Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
90 lines
5.1 KiB
Markdown
90 lines
5.1 KiB
Markdown
# M3 — Sims hub (market prediction simulations)
|
||
|
||
## 1. Component
|
||
The economy organ's prediction engine: **always-running simulations** ("Sims") populated by
|
||
autonomous simulation agents ("Pops") that model market dynamics across multiple mathematical
|
||
domains and time scales. Sims are **queryable at any time** by Traders (M5) — they produce
|
||
**predictions with explicit upper and lower bounds** on every output value. This is the hub spec;
|
||
individual sim types have dedicated sub-specs (M3a–M3g).
|
||
|
||
The academic foundations span AMM mechanism design [1,2], MEV game theory [3,4,5], macro
|
||
tokenomics via SDEs [6,7], and evolutionary consensus games [8–11].
|
||
|
||
## 2. Status / certainty
|
||
DESIGN-FIRST · ABSENT. Role C3; implementation C1. Mathematical foundations C4 (literature
|
||
established); specific model parameters C1.
|
||
|
||
## 3. Language & location
|
||
TBD · `src/economy/sims/`. Numerical computing (Julia, Python/NumPy, Octave, or Rust) for the
|
||
simulation cores. A query facade accessible to Traders. Each sim type (M3a–M3g) may use a
|
||
different runtime suited to its math.
|
||
|
||
## 4. Does / does-not
|
||
- **Does:** run continuously at **≥ 360:1 speed** (360 simulated seconds per wall-clock second)
|
||
across **six concurrent time horizons** — tick/hourly, daily, weekly, monthly, annual, and
|
||
5-year forecast windows; maintain populations of Pops whose behaviors emerge from the sim's
|
||
mathematical model; ingest live data from Data Feeds (M2) for calibration; respond to Trader
|
||
queries with bounded predictions; produce outputs with **explicit upper/lower bounds** on
|
||
every prediction value.
|
||
| Horizon | Window | Sim cadence at 360:1 |
|
||
|---------|--------|---------------------|
|
||
| Tick–hourly | Next 1–60 min | Real-time (360 sim-sec/s) |
|
||
| Daily | Next 24h | 4 sim-minutes per wall-second |
|
||
| Weekly | Next 7d | ~28 sim-minutes per wall-second |
|
||
| Monthly | Next 30d | ~2 sim-hours per wall-second |
|
||
| Annual | Next 365d | ~1 sim-day per wall-second |
|
||
| 5-year | Next 1825d | ~5 sim-days per wall-second |
|
||
- **Does-not:** trade (Traders/Marketplace do); make decisions for traders (it informs, they
|
||
decide); enforce laws (Marketplace does); supervise behavior (Conductor/SAE do); run slower
|
||
than 360:1.
|
||
|
||
## 5. Interface contract
|
||
- `query(sim_type: SimType, query: PredictionQuery) -> BoundedPrediction`.
|
||
`SimType` ∈ { `statistical`, `sociological`, `amm_liquidity`, `mev_adversarial`,
|
||
`tokenomics_macro`, `consensus_staking`, `market_microstructure` } (M3a–M3g).
|
||
- `BoundedPrediction { value, lower_bound, upper_bound, confidence, time_horizon, sim_type, timestamp }`.
|
||
Every output is bounded — no point estimates without uncertainty ranges.
|
||
Example: `{ value: 7.2, lower_bound: 5.8, upper_bound: 8.9, confidence: 0.73,
|
||
time_horizon: "4h", sim_type: "amm_liquidity" }`.
|
||
- `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.
|
||
|
||
## 6. Dependencies & stubs
|
||
- M2 Data Feeds — calibration data source; *stub:* canned market data.
|
||
- M5 Traders — query consumers; *stub:* canned queries.
|
||
- M3a–M3g sub-specs — individual sim implementations; *stub:* each returns fixed predictions.
|
||
|
||
## 7. Invariants / laws
|
||
- **L1 (C5):** sims are **always running** — they are not invoked on demand. Traders query
|
||
current state; they don't trigger computation.
|
||
- **L2 (C5):** every prediction output includes **explicit upper and lower bounds** — no
|
||
unbounded point estimates. Uncertainty is a first-class value, not an afterthought.
|
||
- **L3 (C5):** sims advance at a **minimum speed of 360:1** — 360 simulated seconds per 1
|
||
wall-clock second. Sims may run faster but never slower. This ensures predictions stay
|
||
ahead of real-time market state across all horizons.
|
||
- **L4 (C4):** sims are **read-only from traders' perspective** — a query never mutates sim
|
||
state. Calibration happens only from Data Feeds (M2).
|
||
- **L5 (C4):** each sim type is **independent** — failure in one sim does not cascade to others.
|
||
Degraded sims report their status; traders handle missing predictions.
|
||
- **L6 (C3):** Pops are **simulation constructs, not AI actors** — they follow mathematical
|
||
rules within the sim. Traders (M5) are the AI actors.
|
||
|
||
## 8. Build steps
|
||
1. Define `BoundedPrediction` shape and query protocol.
|
||
2. Build the sim runner (lifecycle management for always-on sims).
|
||
3. Wire M2 Data Feeds → calibration pipeline.
|
||
4. Implement sub-specs M3a–M3g as they land.
|
||
5. Wire trader query interface.
|
||
|
||
## 9. Tests
|
||
Always-on: sim running after init without external trigger. Bounded output: every prediction has
|
||
lower ≤ value ≤ upper. Query: trader receives prediction without mutating sim. Independence:
|
||
one sim's failure doesn't affect others. Calibration: new data updates model state.
|
||
|
||
## 10. Open items
|
||
- Pop lifecycle (birth/death/mutation within sims, or fixed populations?).
|
||
- Cross-sim aggregation (do traders query individual sims, or is there a meta-prediction layer?).
|
||
- Calibration frequency per sim type.
|
||
- Computational budget per sim (how much CPU/GPU each can consume).
|