Rewrite M-series: crypto trading engine + market prediction sims

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>

core/docs/plans/M3b-sociological-sims.md

@@ -0,0 +1,67 @@
+# M3b — Sociological & population dynamics sims
+
+## 1. Component
+Sociological simulation: **evolutionary game theory, bounded rationality, sentiment cascades, and
+population dynamics** among market participants. Pops here are **behavioral archetypes** — retail
+herd followers, contrarian whales, MEV searchers, passive LPs — whose strategies evolve under
+selection pressure. Grounded in evolutionary consensus game models [8,9] and bounded-rationality
+coordination frameworks.
+
+## 2. Status / certainty
+DESIGN-FIRST · ABSENT. Evolutionary game-theory foundations C4 (Cornell blockchain cooperation
+literature [8]); pop behavioral models C1.
+
+## 3. Language & location
+TBD · `src/economy/sims/sociological/`. Agent-based modeling frameworks (Mesa/Python, NetLogo,
+or custom). Needs efficient population iteration and strategy mutation.
+
+## 4. Does / does-not
+- **Does:** simulate populations of behavioral archetypes competing in a market; apply
+  evolutionary dynamics (replicator equation, mutation, selection) to strategy distributions;
+  model sentiment cascades (fear/greed contagion across pop clusters); model bounded rationality
+  (pops satisfice, not optimize — they follow heuristics, not perfect strategies); produce
+  bounded predictions on market sentiment, herd behavior thresholds, and coordination breakdowns.
+- **Does-not:** model protocol mechanics (M3c–M3f); compute statistical forecasts (M3a);
+  represent real individuals (pops are archetypes, not profiles).
+
+## 5. Interface contract
+- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
+- **Output bounds:** population-fraction ranges and sentiment scales.
+  Example: `{ value: 7.3, lower_bound: 5.0, upper_bound: 9.1, confidence: 0.68,
+  time_horizon: "12h", sim_type: "sociological" }` — herd-panic index on a 0–10 scale.
+  Example: `{ value: 0.42, lower_bound: 0.31, upper_bound: 0.55, confidence: 0.72,
+  time_horizon: "1w", sim_type: "sociological" }` — fraction of pops in "contrarian" strategy.
+- **Prediction types:** `sentiment_index`, `herd_threshold`, `strategy_distribution`,
+  `cascade_probability`, `coordination_stability`.
+- Calibration: ingests `rss_news` (sentiment signal) and `price_tick` (realized behavior) from M2.
+
+## 6. Dependencies & stubs
+- M2 Data Feeds — sentiment and price data for calibration; *stub:* canned sentiment series.
+- M3 Sims hub — lifecycle management; *stub:* manual init.
+
+## 7. Invariants / laws
+- **L1 (C4):** pops are **archetypes, not individuals** — no attempt to model or track real
+  market participants. The sim models emergent behavior from strategy populations.
+- **L2 (C4):** strategies **evolve** — the population distribution shifts over time via
+  replicator dynamics. No fixed strategy ratios.
+- **L3 (C3):** bounded rationality is the **default** — pops satisfice with heuristics, not
+  optimize with perfect information. Rational-agent models are a special case, not the baseline.
+
+## 8. Build steps
+1. Define pop archetypes and their heuristic strategies.
+2. Implement replicator dynamics (strategy evolution over generations).
+3. Implement sentiment contagion model (network-based cascade).
+4. Wire M2 news/price data → calibration of pop parameters.
+5. Implement `BoundedPrediction` output with population-fraction CIs.
+
+## 9. Tests
+Evolution: dominant strategy shifts when payoff landscape changes. Cascade: sentiment shock
+propagates through pop network above threshold, not below. Bounded rationality: satisficing pop
+underperforms optimizer in simple games but outperforms in noisy environments. Bounds: all
+outputs include upper/lower.
+
+## 10. Open items
+- Pop archetype catalog (which behavioral types? how many?).
+- Network topology for sentiment contagion (small-world? scale-free?).
+- Calibration from real market data — how to infer pop distribution from observable price action.
+- Cross-sim interaction: do sociological predictions feed into M3c (AMM) or M3d (MEV)?