*Able Markets Skill

Trit: 0 (ERGODIC - coordinates market equilibrium)
Color: #19A845 (seed=137, index=1)
Foundation: Protocol Evolution Markets + *Able Quadrant + WEV

Overview

Prediction markets for protocol proposal outcomes across Swift Evolution, Aptos AIPs, and Scheme SRFIs. Financializes standard possible worlds through the *Able trait taxonomy.

*Able Quadrant Encoding

Derived *Able Traits from Proposals

AIP-137 → PQ-Signable    (#5FE0B8, trit=0)
AIP-129 → Replay-Protectable (#E2F25B, trit=+1)
SRFI-265 → CFG-Composable
SE-0475 → Observable

Active Draft Proposals

Aptos AIPs (High Priority)

Swift Evolution

Scheme SRFIs

Market Mechanics

LMSR Pricing

import math

class AbleMarket:
    """LMSR market for *Able proposal outcomes."""
    
    def __init__(self, proposal_id: str, liquidity: float = 1000.0):
        self.proposal_id = proposal_id
        self.liquidity = liquidity  # b parameter
        self.yes_shares = 0.0
        self.no_shares = 0.0
    
    def cost(self) -> float:
        """Current cost function C(q)."""
        return self.liquidity * math.log(
            math.exp(self.yes_shares / self.liquidity) +
            math.exp(self.no_shares / self.liquidity)
        )
    
    def price_yes(self) -> float:
        """Probability estimate for acceptance."""
        exp_yes = math.exp(self.yes_shares / self.liquidity)
        exp_no = math.exp(self.no_shares / self.liquidity)
        return exp_yes / (exp_yes + exp_no)
    
    def buy_yes(self, amount: float) -> float:
        """Buy YES shares, return cost."""
        old_cost = self.cost()
        self.yes_shares += amount
        return self.cost() - old_cost
    
    def buy_no(self, amount: float) -> float:
        """Buy NO shares, return cost."""
        old_cost = self.cost()
        self.no_shares += amount
        return self.cost() - old_cost

WEV Extraction Formula

WEV(W₀ → W₁) = Σᵢ [V(entityᵢ, W₁) - V(entityᵢ, W₀)] × P(transition)

Where:
- W₀ = pre-proposal world state
- W₁ = post-proposal world state  
- V(entity, W) = value of entity in world W
- P(transition) = market-implied probability

Example: AIP-137 WEV Calculation

def calculate_aip137_wev():
    accounts_at_risk = 10_000_000      # Ed25519 accounts
    avg_value_apt = 100                 # Average APT per account
    crqc_probability = 0.15             # Market consensus
    value_at_risk_ratio = 0.001         # Fraction lost to quantum attack
    
    wev = accounts_at_risk * avg_value_apt * crqc_probability * value_at_risk_ratio
    return wev  # ~150,000 APT

Oracle Resolution

GitHub Status Polling

interface ProposalOracle {
  ecosystem: 'aptos' | 'swift' | 'srfi';
  
  async pollStatus(proposalId: string): Promise<{
    status: 'draft' | 'review' | 'accepted' | 'rejected';
    lastUpdated: Date;
    sourceHash: string;
  }>;
  
  async submitAttestation(
    proposalId: string,
    outcome: boolean,
    proof: string
  ): Promise<void>;
}

// Aptos AIP oracle
const aipOracle: ProposalOracle = {
  ecosystem: 'aptos',
  
  async pollStatus(aipNumber: string) {
    const url = `https://api.github.com/repos/aptos-foundation/AIPs/contents/aips/aip-${aipNumber}.md`;
    const response = await fetch(url);
    const content = atob((await response.json()).content);
    
    const statusMatch = content.match(/Status:\s*(\w+)/i);
    return {
      status: statusMatch?.[1].toLowerCase(),
      lastUpdated: new Date(),
      sourceHash: sha256(content)
    };
  }
};

Multi-Sig Resolution (Move)

module able_markets::oracle {
    use std::vector;
    
    struct OracleConfig has key {
        signers: vector<address>,
        threshold: u64,  // e.g., 3-of-5
    }
    
    struct Attestation has store {
        proposal_id: vector<u8>,
        outcome: bool,
        source_hash: vector<u8>,
        attester: address,
    }
    
    public fun resolve(
        config: &OracleConfig,
        attestations: &vector<Attestation>,
        proposal_id: vector<u8>
    ): bool {
        let matching = count_matching(attestations, &proposal_id);
        assert!(matching >= config.threshold, E_INSUFFICIENT_ATTESTATIONS);
        
        // Majority outcome wins
        let yes_count = count_outcome(attestations, &proposal_id, true);
        yes_count > matching / 2
    }
}

self→Self Autopoietic Loop

The *Able pattern exhibits autopoiesis:

self (proposal instance)
    │
    ├──produces──► Self (protocol trait type)
    │                    │
    │                    ▼
    │              Self ──reconstitutes──► self (new proposals extending it)
    │                                           │
    └───────────────────────────────────────────┘

Market Dynamics Map

Signable

PQ-Signable

Signable

Cross-Ecosystem Arbitrage

def cross_ecosystem_signal(swift_price: float, aptos_price: float) -> str:
    """
    If Swift adopts a pattern, Aptos often follows.
    Arbitrage the information asymmetry.
    """
    if swift_price > 0.7 and aptos_price < 0.4:
        return "BUY Aptos analog - Swift leading indicator"
    elif aptos_price > 0.8 and swift_price < 0.3:
        return "BUY Swift analog - Aptos leading indicator"
    else:
        return "No arbitrage opportunity"

GF(3) Market Conservation

Long (+1) + Market Maker (0) + Short (-1) = 0

For every bullish bet, there's a bearish counterparty.
The market maker provides liquidity but takes no directional risk.
Value is conserved across the market.

DuckDB Schema

CREATE TABLE able_proposals (
    proposal_id VARCHAR PRIMARY KEY,
    ecosystem VARCHAR,          -- 'aptos', 'swift', 'srfi'
    title VARCHAR,
    status VARCHAR,             -- 'draft', 'review', 'accepted', 'rejected'
    able_trait VARCHAR,         -- '*Able trait name
    trit INT,                   -- -1, 0, +1
    color VARCHAR,              -- Hex color from Gay.jl
    last_call_date DATE,
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

CREATE TABLE market_state (
    proposal_id VARCHAR REFERENCES able_proposals(proposal_id),
    yes_shares FLOAT,
    no_shares FLOAT,
    liquidity FLOAT,
    implied_probability FLOAT,
    total_volume FLOAT,
    last_trade_at TIMESTAMP
);

CREATE TABLE attestations (
    attestation_id VARCHAR PRIMARY KEY,
    proposal_id VARCHAR,
    outcome BOOLEAN,
    attester VARCHAR,
    source_hash VARCHAR,
    created_at TIMESTAMP
);

-- Query: Find arbitrage opportunities
SELECT 
    a.proposal_id,
    a.ecosystem,
    m.implied_probability,
    a.able_trait,
    CASE 
        WHEN m.implied_probability < 0.3 AND a.status = 'review' 
        THEN 'UNDERPRICED'
        WHEN m.implied_probability > 0.8 AND a.status = 'draft'
        THEN 'OVERPRICED'
        ELSE 'FAIR'
    END as signal
FROM able_proposals a
JOIN market_state m ON a.proposal_id = m.proposal_id
WHERE a.status IN ('draft', 'review');

Canonical Triads

Queryable (-1) ⊗ Derangeable (0) ⊗ Replay-Protectable (+1) = 0 ✓
bisimulation-game (-1) ⊗ able-markets (0) ⊗ gay-mcp (+1) = 0 ✓

Asymmetric Bets: High Impact, Low Probability

The market systematically underprices low-probability, high-impact proposals.

Top Asymmetric Opportunities

Asymmetric Score Formula

AsymScore = WEV × (1 - P) / P

High score = high impact × low probability

Half-Kelly Portfolio (1000 APT)

AIP-130 (Signed Int):  125 APT (12.5%)  - Highest E[V]
AIP-125 (Scheduled):    50 APT (5.0%)   - DeFi automation
SE-0495 (@cdecl):      125 APT (12.5%)  - Swift systems
SRFI-265 (CFG):        125 APT (12.5%)  - Parser ecosystem
Cash reserve:          487 APT (48.8%)  - Dry powder

Open Games Formalization

LMSR markets are formalized as compositional open games with Para/Optic structure:

        ┌───────────────┐
  State─│               │─New State    (forward: play)
        │  LMSR Market  │
Utility←│               │←Outcome      (backward: coplay)
        └───────────────┘

Game Composition

;; Sequential: Markets → Arbitrage layer
(sequential-compose markets arbitrage)

;; Parallel: 3 ecosystem markets running concurrently
(parallel-compose
  (parallel-compose aptos-market swift-market)
  scheme-market)

GF(3) Game Classification

Validator (-1) + Arbitrageur (0) + Trader (+1) = 0 ✓

Nash Equilibrium as Fixed Point

((:equilibrium market) state true-probability)
;; => true when market-price ≈ true-probability

Cross-Ecosystem Arbitrage Game

(defn arbitrage-game [market-a market-b correlation]
  ;; Play: Identify mispricing via correlation
  ;; Coplay: Profit from correlated resolution
  ;; Equilibrium: No-arb when prices reflect correlation
  ...)

Run simulation:

bb scripts/open_game.clj simulate
bb scripts/open_game.clj classify

See Also

• open-games

  - Compositional game theory framework

• protocol-evolution-markets

  - Foundation skill for this

• world-extractable-value

  - WEV calculation framework

• gay-mcp

  - Deterministic color generation

• aptos-society

  - On-chain WEV implementation

• multiversal-finance

  - Multiverse trading framework

WEV Derivational Positions

Proposals map to 5 derivational positions from the Unworld model:

GF(3) Conservation: Sum of all trits = -3 ≡ 0 (mod 3) ✓

Proposal → Position Mapping

{:aip-137  {:position :sheaf   :decay true}   ;; PQ-Signable
 :aip-120  {:position :rewrite :decay false}  ;; CLOB-Native
 :aip-130  {:position :fibred  :decay false}  ;; Negatable
 :srfi-265 {:position :rewrite :decay false}} ;; CFG-Composable

Kelly Betting with Lazy Knowledge

Front-run information asymmetry by betting on intermediate milestones:

Expected ROI: 104% on 800 APT allocated

Usage

# Poll current proposal statuses
bb poll.clj poll

# Calculate WEV for a proposal
bb poll.clj wev AIP-137 0.65

# Run market simulation
bb poll.clj simulate

# Open Games formalization
bb scripts/open_game.clj simulate

# WEV derivational positions
bb scripts/wev_positions.clj positions
bb scripts/wev_positions.clj kelly
bb scripts/wev_positions.clj wev AIP-137

# Asymmetric bets analysis
bb scripts/asymmetric_bets.clj