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