Enrich M3 sim sub-specs with 11 discovered frameworks and 360:1 minimum speed

- 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>

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

@@ -1,38 +1,75 @@
 # 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.
+Sociological simulation: **evolutionary game theory, bounded rationality, opinion dynamics,
+complex contagion, adaptive learning populations, and Mean-Field Game equilibria** among market
+participants. Pops here are **behavioral archetypes** — retail herd followers, contrarian whales,
+MEV searchers, passive LPs, pump-and-dump manipulators — whose strategies evolve under selection
+pressure across **six concurrent time horizons**. Grounded in evolutionary consensus game models
+[8,9], Hegselmann-Krause opinion dynamics (2002), complex contagion theory (Centola & Macy 2007),
+MFG theory (Lasry & Lions 2007), and crypto manipulation ABMs.
 
 ## 2. Status / certainty
-DESIGN-FIRST · ABSENT. Evolutionary game-theory foundations C4 (Cornell blockchain cooperation
-literature [8]); pop behavioral models C1.
+DESIGN-FIRST · ABSENT. Evolutionary game theory C4 (Cornell [8]). Hegselmann-Krause bounded
+confidence C5 (established 2002). Complex contagion C4 (Centola & Macy 2007; crypto applications
+C3). Bandit-replicator hybrid C3 (emerging). Mean-Field Games C4 (Lasry & Lions 2007; tensor-train
+solvers C3). Crypto pump-and-dump ABM C3 (3-agent protocol validated on historical data).
+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.
+or custom). Needs efficient population iteration, strategy mutation, PDE solvers for MFG
+(HJB + Fokker-Planck), and bandit algorithms (UCB/Thompson).
 
 ## 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.
+  **replicator dynamics** $dx_i/dt = x_i(\pi_i(x) - \bar{\pi}(x))$ to strategy distributions;
+  model **opinion clustering** via Hegselmann-Krause bounded confidence:
+  $x_i(t+1) = x_i(t) + \mu(x_j(t) - x_i(t))$ for $|x_i - x_j| \leq d$ — agents only update
+  toward neighbors within confidence bound $d$, creating natural clustering and trend-reversal
+  thresholds; model **complex contagion** with heterogeneous thresholds: adoption probability
+  $P_i = f(n_i / k_i)$ where multiple exposures amplify adoption non-linearly (captures meme-coin
+  rallies and narrative-driven pumps); implement **bandit-replicator hybrid** where pops use
+  UCB or Thompson Sampling to estimate strategy payoffs:
+  $x_i'(t) = x_i(t)[\lambda_i(t) - \bar{\lambda}(t)]$ with $\lambda_i = \text{UCB}(\theta_i)$
+  — bridges replicator dynamics with multi-armed bandit learning; solve **Mean-Field Game
+  equilibria** via coupled HJB + Fokker-Planck PDEs for large-population limits:
+  $-\partial_t u + H(x, \nabla u) = F(x, m)$ (HJB, individual optimization),
+  $\partial_t m - \nabla \cdot (m \nabla_p H) = 0$ (Fokker-Planck, population density) — Newton
+  iteration with tensor-train decomposition reduces $O(N^d)$ to $O(dNr^2)$ for high-dimensional
+  state spaces; simulate **crypto pump-and-dump protocol** with 3 pop types: Normal traders,
+  Market Analysts (MA, information-advantaged), Market Players (MP, manipulators) in a 4-phase
+  cycle (accumulation → promotion → distribution → collapse); model sentiment cascades
+  (fear/greed contagion across pop clusters); model bounded rationality (pops satisfice, not
+  optimize — heuristics, not perfect strategies); produce bounded predictions across all six
+  time horizons.
 - **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,
+- **Output bounds:** population-fraction ranges, sentiment scales, MFG equilibrium stability.
+- **Time-horizon mapping** (all run concurrently):
+  | Horizon | Primary models | Update cadence |
+  |---------|---------------|----------------|
+  | Hourly | Hegselmann-Krause opinion clusters, bandit-replicator | Every data tick |
+  | Daily | Complex contagion cascades, pump-and-dump phase detection | Hourly roll |
+  | Weekly | Replicator dynamics strategy evolution, MFG equilibrium | Daily roll |
+  | Monthly | Population archetype composition, narrative regime shifts | Weekly roll |
+  | Annual | Long-run evolutionary stable strategies (ESS) | Monthly roll |
+  | 5-year | MFG stationary equilibria, structural population shifts | Quarterly roll |
+- Examples:
+  `{ value: 7.3, lower_bound: 5.0, upper_bound: 9.1, confidence: 0.68,
+  time_horizon: "12h", sim_type: "sociological" }` — herd-panic index (0–10).
+  `{ 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.
+  `{ value: "promotion", lower_bound: null, upper_bound: null, confidence: 0.61,
+  time_horizon: "current", sim_type: "sociological" }` — pump-and-dump phase detection.
+  `{ value: 0.78, lower_bound: 0.65, upper_bound: 0.88, confidence: 0.70,
+  time_horizon: "30d", sim_type: "sociological" }` — MFG equilibrium stability index.
 - **Prediction types:** `sentiment_index`, `herd_threshold`, `strategy_distribution`,
-  `cascade_probability`, `coordination_stability`.
+  `cascade_probability`, `coordination_stability`, `opinion_cluster_count`,
+  `pump_dump_phase`, `mfg_equilibrium_stability`, `narrative_regime`.
 - Calibration: ingests `rss_news` (sentiment signal) and `price_tick` (realized behavior) from M2.
 
 ## 6. Dependencies & stubs
@@ -40,28 +77,43 @@ or custom). Needs efficient population iteration and strategy mutation.
 - 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
+- **L1 (C5):** 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
+- **L2 (C5):** 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
+- **L3 (C4):** bounded rationality is the **default** — pops satisfice with heuristics, not
   optimize with perfect information. Rational-agent models are a special case, not the baseline.
+- **L4 (C4):** **complex contagion requires multiple exposures** — adoption is non-linear in
+  neighbor count, not simple diffusion. Single-exposure models undercount threshold effects.
+- **L5 (C4):** the MFG limit is **valid only for large populations** — below ~100 pops, use
+  discrete replicator dynamics; above, the continuum HJB+FP approximation applies.
+- **L6 (C3):** pump-and-dump detection is **phase-based** — the 4-phase cycle (accumulate →
+  promote → distribute → collapse) has distinct statistical signatures in volume and price.
 
 ## 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.
+3. Implement Hegselmann-Krause bounded confidence opinion model.
+4. Implement complex contagion with heterogeneous thresholds.
+5. Implement bandit-replicator hybrid (UCB payoff estimation + replicator selection).
+6. Implement MFG solver (HJB + Fokker-Planck with Newton iteration).
+7. Implement pump-and-dump 3-type ABM (Normal, MA, MP) with 4-phase protocol.
+8. Wire M2 news/price data → calibration of pop parameters.
+9. Implement multi-horizon `BoundedPrediction` output.
 
 ## 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.
+propagates through pop network above threshold, not below. Bounded confidence: opinion clusters
+form at predicted cluster count for given $d$. Complex contagion: multiple-exposure requirement
+produces slower but more robust adoption than simple contagion. Bandit: explore-exploit tradeoff
+produces adapting populations. MFG: equilibrium converges for large N; matches discrete sim for
+small N. Pump-dump: 4-phase cycle detected on synthetic manipulation data. 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.
+- MFG tensor-train rank $r$ (accuracy vs. compute tradeoff).
+- Hegselmann-Krause confidence bound $d$ — fixed or adaptive?
 - Cross-sim interaction: do sociological predictions feed into M3c (AMM) or M3d (MEV)?