sica-fondt/core/docs/plans/M3d-mev-adversarial-sims.md

M3d — MEV & adversarial extraction sims

1. Component

Maximal Extractable Value simulation: models transaction ordering as an optimization problem, Priority Gas Auctions (PGA) as all-pay auctions, and block building as a multidimensional knapsack problem. Pops here are searcher bots, block builders, and validators competing for extractable value. Grounded in ACM MEV game theory [3] and knapsack auction literature [4,5].

2. Status / certainty

DESIGN-FIRST · ABSENT. PGA-as-all-pay-auction model C4 (ACM [3]); knapsack formulation C4 (Cornell [4,5]); simulation parameterization C1.

3. Language & location

TBD · src/economy/sims/mev/. Needs combinatorial optimization (for knapsack) and continuous-time auction modeling. Python (PuLP/OR-Tools for optimization), Rust, or Julia.

4. Does / does-not

• Does: simulate Priority Gas Auctions where multiple searcher bots compete for the same arbitrage opportunity V by bidding gas fees g in a continuous-time all-pay auction; model block building as a multidimensional knapsack problem (scarce block space, heterogeneous transaction values/sizes); simulate endogenous selection cutoffs under paid-priority ordering; predict MEV exposure for proposed trades; produce bounded predictions on extraction risk and optimal gas strategies.
• Does-not: extract MEV itself (this is a simulator, not a searcher); model AMM mechanics (M3c handles pool math); model social dynamics (M3b).

5. Interface contract

• Implements query(PredictionQuery) -> BoundedPrediction per M3 hub.
• Output bounds: extraction probability ranges and gas cost intervals. Example: { value: 0.23, lower_bound: 0.11, upper_bound: 0.38, confidence: 0.80, time_horizon: "next_block", sim_type: "mev_adversarial" } — probability this trade gets sandwiched. Example: { value: 14.7, lower_bound: 8.2, upper_bound: 22.5, confidence: 0.75, time_horizon: "next_block", sim_type: "mev_adversarial" } — optimal gas bid (gwei) for a given opportunity.
• Prediction types: sandwich_probability, frontrun_risk, optimal_gas_bid, block_inclusion_probability, mev_exposure.
• Calibration: ingests on_chain_event (mempool-like data) and price_tick from M2.

6. Dependencies & stubs

• M2 Data Feeds — on-chain events and gas data; stub: canned mempool snapshots.
• M3 Sims hub — lifecycle management; stub: manual init.
• M3c AMM sims — pool state for arbitrage opportunity detection; stub: fixed pool state.

7. Invariants / laws

• L1 (C4): PGA is modeled as an all-pay auction — all bidders pay their gas whether they win or not. The sim must capture this cost structure (not winner-pays-only).
• L2 (C4): block building is a knapsack problem, not a queue — builders optimize for total extracted value subject to gas limit constraints, not first-come-first-served.
• L3 (C3): MEV exposure predictions are pre-trade — traders query this sim before submitting to the Marketplace to understand their extraction risk.

8. Build steps

1. Implement the PGA all-pay auction model (N searchers, opportunity value V, gas bids).
2. Implement the block-building knapsack solver.
3. Add sandwich/frontrun detection heuristics.
4. Wire M2 on-chain data → calibration of searcher population and gas dynamics.
5. Wire pre-trade query interface for Traders.

9. Tests

All-pay: losing bidders still pay gas cost. Knapsack: builder selects optimal transaction set under gas limit. Sandwich: known sandwich-vulnerable trade flagged; non-vulnerable trade clear. Bounds: all outputs bounded. Pre-trade: query does not submit any transaction.

10. Open items

• Mempool data access (public mempool? private order flow?).
• Which MEV types to model initially (sandwich, backrun, liquidation, JIT?).
• Multi-block MEV (cross-block extraction strategies).
• Integration with M3c (arbitrage opportunities arise from AMM pool state).