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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>
70 lines
3.9 KiB
Markdown
70 lines
3.9 KiB
Markdown
# M3d — MEV & adversarial extraction sims
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## 1. Component
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Maximal Extractable Value simulation: models **transaction ordering as an optimization problem**,
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**Priority Gas Auctions (PGA) as all-pay auctions**, and **block building as a multidimensional
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knapsack problem**. Pops here are **searcher bots, block builders, and validators** competing for
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extractable value. Grounded in ACM MEV game theory [3] and knapsack auction literature [4,5].
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## 2. Status / certainty
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DESIGN-FIRST · ABSENT. PGA-as-all-pay-auction model C4 (ACM [3]); knapsack formulation C4
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(Cornell [4,5]); simulation parameterization C1.
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## 3. Language & location
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TBD · `src/economy/sims/mev/`. Needs combinatorial optimization (for knapsack) and continuous-time
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auction modeling. Python (PuLP/OR-Tools for optimization), Rust, or Julia.
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## 4. Does / does-not
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- **Does:** simulate Priority Gas Auctions where multiple searcher bots compete for the same
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arbitrage opportunity $V$ by bidding gas fees $g$ in a continuous-time all-pay auction; model
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block building as a multidimensional knapsack problem (scarce block space, heterogeneous
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transaction values/sizes); simulate endogenous selection cutoffs under paid-priority ordering;
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predict MEV exposure for proposed trades; produce bounded predictions on extraction risk and
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optimal gas strategies.
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- **Does-not:** extract MEV itself (this is a simulator, not a searcher); model AMM mechanics
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(M3c handles pool math); model social dynamics (M3b).
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## 5. Interface contract
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- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
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- **Output bounds:** extraction probability ranges and gas cost intervals.
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Example: `{ value: 0.23, lower_bound: 0.11, upper_bound: 0.38, confidence: 0.80,
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time_horizon: "next_block", sim_type: "mev_adversarial" }` — probability this trade gets
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sandwiched.
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Example: `{ value: 14.7, lower_bound: 8.2, upper_bound: 22.5, confidence: 0.75,
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time_horizon: "next_block", sim_type: "mev_adversarial" }` — optimal gas bid (gwei) for
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a given opportunity.
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- **Prediction types:** `sandwich_probability`, `frontrun_risk`, `optimal_gas_bid`,
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`block_inclusion_probability`, `mev_exposure`.
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- Calibration: ingests `on_chain_event` (mempool-like data) and `price_tick` from M2.
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## 6. Dependencies & stubs
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- M2 Data Feeds — on-chain events and gas data; *stub:* canned mempool snapshots.
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- M3 Sims hub — lifecycle management; *stub:* manual init.
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- M3c AMM sims — pool state for arbitrage opportunity detection; *stub:* fixed pool state.
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## 7. Invariants / laws
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- **L1 (C4):** PGA is modeled as an **all-pay auction** — all bidders pay their gas whether they
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win or not. The sim must capture this cost structure (not winner-pays-only).
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- **L2 (C4):** block building is a **knapsack problem, not a queue** — builders optimize for
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total extracted value subject to gas limit constraints, not first-come-first-served.
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- **L3 (C3):** MEV exposure predictions are **pre-trade** — traders query this sim *before*
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submitting to the Marketplace to understand their extraction risk.
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## 8. Build steps
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1. Implement the PGA all-pay auction model (N searchers, opportunity value V, gas bids).
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2. Implement the block-building knapsack solver.
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3. Add sandwich/frontrun detection heuristics.
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4. Wire M2 on-chain data → calibration of searcher population and gas dynamics.
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5. Wire pre-trade query interface for Traders.
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## 9. Tests
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All-pay: losing bidders still pay gas cost. Knapsack: builder selects optimal transaction set
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under gas limit. Sandwich: known sandwich-vulnerable trade flagged; non-vulnerable trade clear.
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Bounds: all outputs bounded. Pre-trade: query does not submit any transaction.
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## 10. Open items
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- Mempool data access (public mempool? private order flow?).
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- Which MEV types to model initially (sandwich, backrun, liquidation, JIT?).
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- Multi-block MEV (cross-block extraction strategies).
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- Integration with M3c (arbitrage opportunities arise from AMM pool state).
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