mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 00:23:15 +00:00
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>
73 lines
4.2 KiB
Markdown
73 lines
4.2 KiB
Markdown
# M3e — Tokenomics & macro-state sims
|
|
|
|
## 1. Component
|
|
Macro-level token economy simulation: models **token supply dynamics, monetary policy (halvings,
|
|
burns, inflation), and systemic stock-flow balances** using stochastic differential equations
|
|
(SDEs) and state-space models. Pops here are **aggregate behavioral cohorts** (miners/validators,
|
|
holders, speculators, protocol treasuries) whose collective behavior drives token-level dynamics.
|
|
Grounded in the Vienna University complex-systems token modeling [7] and ResearchGate engineering
|
|
token economy frameworks [6].
|
|
|
|
## 2. Status / certainty
|
|
DESIGN-FIRST · ABSENT. SDE state-space framework C4 (Vienna [7]); stock-flow modeling C4
|
|
(ResearchGate [6]); specific token model parameters C1.
|
|
|
|
## 3. Language & location
|
|
TBD · `src/economy/sims/tokenomics/`. Needs SDE solvers (Euler-Maruyama, Milstein) and
|
|
state-space estimation. Julia (DifferentialEquations.jl), Python (scipy), or Octave.
|
|
|
|
## 4. Does / does-not
|
|
- **Does:** simulate token state dynamics via the SDE framework:
|
|
$dX_t = f(X_t, u(X_t, t), t)dt + \sigma(X_t, t)dW_t$ where $X_t$ is the system state vector,
|
|
$u$ is the behavioral policy function, deterministic drift captures programmatic parameters
|
|
(halvings, burns), and Brownian motion $\sigma dW_t$ captures stochastic behavioral shocks;
|
|
model stock-flow balances (circulating supply, staked, locked, burned); simulate monetary
|
|
policy impacts (halving events, fee burns, treasury emissions); produce bounded predictions
|
|
on token supply trajectories, inflation rates, and velocity.
|
|
- **Does-not:** model individual transactions (M3c/M3d); model social sentiment (M3b);
|
|
model consensus mechanics (M3f).
|
|
|
|
## 5. Interface contract
|
|
- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
|
|
- **Output bounds:** SDE confidence bands (derived from the stochastic component $\sigma dW_t$).
|
|
Example: `{ value: 2.1, lower_bound: 1.4, upper_bound: 3.2, confidence: 0.90,
|
|
time_horizon: "90d", sim_type: "tokenomics_macro" }` — annualized inflation rate (%).
|
|
Example: `{ value: 0.67, lower_bound: 0.58, upper_bound: 0.74, confidence: 0.85,
|
|
time_horizon: "30d", sim_type: "tokenomics_macro" }` — staking ratio (fraction of supply).
|
|
- **Prediction types:** `supply_trajectory`, `inflation_rate`, `staking_ratio`,
|
|
`velocity_estimate`, `halving_impact`, `treasury_runway`.
|
|
- Calibration: ingests `on_chain_event` (supply metrics, staking data) from M2.
|
|
|
|
## 6. Dependencies & stubs
|
|
- M2 Data Feeds — on-chain supply/staking data; *stub:* canned supply snapshots.
|
|
- M3 Sims hub — lifecycle management; *stub:* manual init.
|
|
|
|
## 7. Invariants / laws
|
|
- **L1 (C4):** the SDE framework is the **canonical representation** — all token dynamics are
|
|
expressed as drift + diffusion. Deterministic policy (halvings, burns) lives in the drift $f$;
|
|
behavioral uncertainty lives in the diffusion $\sigma dW_t$.
|
|
- **L2 (C4):** **stock-flow conservation** — tokens are never created or destroyed outside the
|
|
protocol's defined mechanisms. The sim must balance: circulating + staked + locked + burned =
|
|
total ever minted.
|
|
- **L3 (C3):** macro sims operate on **aggregate cohorts, not individuals** — the state vector
|
|
$X_t$ tracks population-level quantities (total staked, total circulating), not per-wallet.
|
|
|
|
## 8. Build steps
|
|
1. Implement Euler-Maruyama SDE solver for a simple token model (supply + staking).
|
|
2. Define the state vector $X_t$ and drift/diffusion functions for a reference token.
|
|
3. Add stock-flow accounting (verify conservation).
|
|
4. Wire M2 on-chain data → state estimation / calibration.
|
|
5. Add monetary policy events (halving, burn) as drift discontinuities.
|
|
|
|
## 9. Tests
|
|
SDE: sample paths have correct mean (matches drift) and variance (matches diffusion). Stock-flow:
|
|
conservation holds across all time steps. Halving: supply growth rate drops at halving event.
|
|
Calibration: state estimate converges to observed data. Bounds: SDE confidence bands correctly
|
|
cover realized paths on backtest.
|
|
|
|
## 10. Open items
|
|
- Which tokens to model initially (ETH? BTC? a specific alt?).
|
|
- State vector dimensionality (how many state variables per token model?).
|
|
- Behavioral policy function $u(X_t, t)$ — how to parameterize aggregate cohort behavior.
|
|
- Multi-token interactions (correlated diffusions across tokens?).
|