Skill able-markets
Scanned 9/6/2026
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---
name: able-markets
description: "Skill able-markets"
---
# *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
| Trit | *Able Trait | Market Role | Color |
|------|-------------|-------------|-------|
| -1 | Queryable | Risk hedgers, validators | #9068DA |
| 0 | Derangeable | Market makers, arbitrageurs | #E3EF45 |
| +1 | Colorable | Speculators, early adopters | #C335A8 |
### 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)
| AIP | Title | Status | Last-Call | *Able Trait |
|-----|-------|--------|-----------|-------------|
| 137 | Post-Quantum SLH-DSA | Draft | 02/09/2026 | PQ-Signable |
| 129 | Orderless Transactions | Draft | June 2025 | Replay-Protectable |
| 113 | Domain-based Account Abstraction | Active | - | Derivable |
### Swift Evolution
| SE | Title | Status | *Able Trait |
|----|-------|--------|-------------|
| 0475 | Transactional Observation | In Review | Observable |
### Scheme SRFIs
| SRFI | Title | Status | *Able Trait |
|------|-------|--------|-------------|
| 265 | CFG Language | Draft 2025-10-30 | CFG-Composable |
| 263 | Prototype Object System | Draft 2025-06-09 | Prototype-Clonable |
## Market Mechanics
### LMSR Pricing
```python
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
```python
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
```typescript
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)
```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
| Phase | self | Self | Market State |
|-------|------|------|--------------|
| Draft | AIP-137 text | `Signable` concept | Initial liquidity |
| Review | Community feedback | Refinements | Price discovery |
| Accept | Merged code | `PQ-Signable` concrete | Resolution |
| Deploy | Mainnet accounts | All `Signable` instances | WEV extraction |
## Cross-Ecosystem Arbitrage
```python
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
```sql
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
| Proposal | Prob | WEV | Asymmetric Score | Why Low Prob |
|----------|------|-----|------------------|--------------|
| AIP-120 (Trading Engine) | 25% | 36.5M APT | 109.5M | Massive engineering; DEX competition |
| AIP-119 (Reduce Staking) | 15% | 5.5M APT | 31.2M | Validators oppose; reduces income |
| AIP-130 (Signed Integers) | 65% | 50M APT | 26.9M | Compiler changes; testing burden |
| AIP-125 (Scheduled Txs) | 40% | 10M APT | 15.0M | Privacy concerns; complexity |
### 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
```clojure
;; 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
| Trit | Game Role | Examples |
|------|-----------|----------|
| -1 | Validator | Equilibrium checker, auditor |
| 0 | Coordinator | Market maker, arbitrageur |
| +1 | Generator | Trader, liquidity provider |
```
Validator (-1) + Arbitrageur (0) + Trader (+1) = 0 ✓
```
### Nash Equilibrium as Fixed Point
```clojure
((:equilibrium market) state true-probability)
;; => true when market-price ≈ true-probability
```
### Cross-Ecosystem Arbitrage Game
```clojure
(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:**
```bash
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:
| Position | Seed | Trits | Color | SICP | WEV Source |
|----------|------|-------|-------|------|------------|
| FIBRED | d16bb89f | (0,1,-1) | #19F473 | procedures | bridge creation |
| SHEAF | 9c872538 | (0,1,-1) | #1CF66E | state | time windows (decays) |
| DECOMP | 7a1cdf2e | (-1,1,0) | #D704A5 | data | bag membership |
| REWRITE | fe59de39 | (-1,-1,-1) | #7319F4 | metalinguistic | DPO rules |
| AGENTS | 8ac473c8 | (-1,0,1) | #BE01C1 | machines | routing success |
**GF(3) Conservation**: Sum of all trits = -3 ≡ 0 (mod 3) ✓
### Proposal → Position Mapping
```clojure
{: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:
| Strategy | Odds Move | Edge | Half-Kelly | Direction |
|----------|-----------|------|------------|-----------|
| self_funding_bounty | 33%→70% | 37% | 275 APT | YES |
| early_champion_signal | 33%→60% | 27% | 225 APT | YES |
| bounded_progress_dca | 33%→55% | 22% | 175 APT | HEDGE |
| antihydra_hedge | 67%→85% | 18% | 125 APT | NO |
**Expected ROI**: 104% on 800 APT allocated
## Usage
```bash
# 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
```
## Para(Optic) atlas
Part of: `para-mensch-commons`.
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