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Hourly–weekly: full window in ≤1 wall-second (3600:1 to 604800:1). Monthly: daily ticks, 30s wall for full window. Annual: weekly ticks, ~52s wall. 5-year: monthly ticks, ~60s wall. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
92 lines
5.3 KiB
Markdown
92 lines
5.3 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 are **queryable at any time** by Traders (M5) — they produce
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**predictions with explicit upper and lower bounds** on every output value. This is the hub spec;
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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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TBD · `src/economy/sims/`. Numerical computing (Julia, Python/NumPy, Octave, or Rust) for the
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simulation cores. A query facade accessible to Traders. Each sim type (M3a–M3g) may use a
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different runtime suited to its math.
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## 4. Does / does-not
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- **Does:** tick-advance continuously at **≥ 360:1** (1 wall-second = 1 sim-hour minimum)
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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; respond to Trader queries with bounded predictions; produce outputs with
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**explicit upper/lower bounds** on every prediction value.
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| Horizon | Window | Speed (1s wall =) | Ratio | Wall time for window |
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|---------|--------|-------------------|-------|---------------------|
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| Tick–hourly | Next 1–60 min | 1h sim | 3,600:1 | <1s |
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| Daily | Next 24h | 1d sim | 86,400:1 | 1s |
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| Weekly | Next 7d | 1w sim | 604,800:1 | 1s |
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| Monthly | Next 30d | 1d sim | 86,400:1 | 30s |
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| Annual | Next 365d | 1w sim | 604,800:1 | ~52s |
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| 5-year | Next 1825d | 1mo sim | 2,592,000:1 | ~60s |
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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); skip ticks;
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run slower than 360:1.
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## 5. Interface contract
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- `query(sim_type: SimType, query: PredictionQuery) -> BoundedPrediction`.
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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, time_horizon, sim_type, timestamp }`.
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Every output is bounded — no point estimates without uncertainty ranges.
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Example: `{ value: 7.2, lower_bound: 5.8, upper_bound: 8.9, confidence: 0.73,
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time_horizon: "4h", sim_type: "amm_liquidity" }`.
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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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- M5 Traders — query consumers; *stub:* canned queries.
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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** — they advance every tick, never
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skip. Each time horizon runs at its own rated speed (see §4 table). Hourly through weekly
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cover their full window in ≤ 1 wall-second; monthly through 5-year dial down to coarser
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ticks (daily/weekly/monthly steps) and take 30–60 wall-seconds for a full window pass.
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No horizon may run slower than its rated speed.
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- **L4 (C4):** sims are **read-only from traders' perspective** — a query never mutates sim
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state. Calibration happens only from Data Feeds (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 and query protocol.
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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 trader query interface.
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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. Query: trader receives prediction without mutating sim. Independence:
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one sim's 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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