home / skills / plurigrid / asi / levin-levity
This skill analyzes Levin-levity dynamics to balance exploration and convergence, enabling WEV pricing and BB(n) insights for proof markets.
npx playbooks add skill plurigrid/asi --skill levin-levityReview the files below or copy the command above to add this skill to your agents.
---
name: levin-levity
description: 'Leonid Levin''''s algorithmic complexity meets playful mutual ingression. Use for: BB(n) prediction markets, Kolmogorov complexity rewards, WEV extraction from proof inefficiencies, Nash equilibrium between exploration (LEVITY) and convergence (LEVIN).'
version: 1.0.0
---
# Levin-Levity: Mutual Ingression of Minds
> "The shortest program that outputs the universe is the universe computing itself."
> — Levin, played lightly
## Core Duality
```
┌─────────────────────────────────────────────────────────────────┐
│ LEVIN ⇌ LEVITY DIALECTIC │
├─────────────────────────────────────────────────────────────────┤
│ LEVIN (-1) │ Convergence, compression, Kolmogorov │
│ │ "Find the shortest program" │
│ │ τ_mix → 0 (rapid equilibration) │
├────────────────┼────────────────────────────────────────────────┤
│ LEVITY (+1) │ Exploration, expansion, serendipity │
│ │ "Discover new programs to compress" │
│ │ τ_mix → ∞ (eternal novelty) │
├────────────────┼────────────────────────────────────────────────┤
│ ERGODIC (0) │ Nash equilibrium of the two │
│ │ "Mutual ingression of minds" │
│ │ τ_mix = τ_optimal (WEV extracted) │
└─────────────────────────────────────────────────────────────────┘
```
## Leonid Levin's Key Ideas
### 1. Universal Search (Levin Search)
The optimal algorithm for inversion problems runs all programs in parallel, weighted by 2^(-|p|):
```
L(x) = min_p { 2^|p| × T(p,x) }
```
where |p| is program length and T(p,x) is runtime. This is **Levin complexity**.
**Levity interpretation**: Run all proofs in parallel, weighted by their Kolmogorov complexity. The first to halt wins the $BEAVER bounty.
### 2. Kolmogorov Complexity
K(x) = length of shortest program producing x
**Connection to BB(n)**:
- BB(n) = max halting output of n-state Turing machines
- K(BB(n)) ≤ O(n) (trivially describable by n)
- But computing BB(n) requires unbounded time
**WEV Insight**: The gap between K(BB(n)) and the actual compute cost is the extractable inefficiency.
### 3. Algorithmic Probability
P(x) = Σ_p 2^(-|p|) for all p that output x
**Levity interpretation**: The probability that a random program outputs your proof. Higher algorithmic probability = lower $BEAVER reward (too easy!).
## The LEVITY-LEVIN Game
From `triplet_1_week2_nash_solver.jl`:
```julia
# Player profiles
LEVITY = RulePerformance(
"LEVITY",
quality = 0.72, # Explores alternative cofactors
exploration = 0.65, # High novelty
cofactor_discovery = 1.0 # Finds new patterns
)
LEVIN = RulePerformance(
"LEVIN",
quality = 0.88, # Fast convergence
exploration = 0.40, # Contracts search space
cofactor_discovery = 0.5 # Rediscovers known patterns
)
# Nash equilibrium
# Neither player can improve by unilateral weight change
w_levity = 0.5
w_levin = 0.5
# ERGODIC: balanced allocation extracts maximum combined payoff
```
### Best Response Dynamics
```
Task Type │ Optimal w_levity │ Optimal w_levin │ Winner
───────────────┼──────────────────┼─────────────────┼────────
discovery │ 0.70 │ 0.30 │ LEVITY
convergence │ 0.30 │ 0.70 │ LEVIN
efficiency │ 0.55 │ 0.45 │ ERGODIC
balanced │ 0.50 │ 0.50 │ NASH
```
## BB(6) World Extractable Value (WEV)
### Mixing Time Analysis
| World | Strategy | τ_mix | Cost | WEV |
|-------|----------|-------|------|-----|
| W_Nash (uncoordinated) | Each prover works alone | years | 100 human-years | — |
| W_Opt (market-coordinated) | $BEAVER prediction market | months | 10 human-years | 90 human-years |
**WEV = C_Nash - C_Opt = 90 human-years of proof effort**
### Extraction Mechanism
```
$BEAVER Token Flow:
Discovery Event → Verification Phase (1 week)
→ Proofs submitted in Lean/Agda
→ Oracle verifies
→ $BEAVER minted ∝ log₃(BB(n))
→ WEV distributed to market participants
```
### Reward Formula
```
BEAVER_REWARD = log₃(BB(n)) × VERIFICATION_MULTIPLIER × TERNARY_BONUS
Where:
log₃(BB(n)) = Kolmogorov complexity in balanced ternary
VERIFICATION_MULTIPLIER = {1.0 (Move), 2.0 (Lean), 3.0 (Agda)}
TERNARY_BONUS = {-1: 0.5×, 0: 1.0×, +1: 1.5×}
```
### Example: BB(5) = 47,176,870
```
log₃(47,176,870) ≈ 16.03 trits
Agda proof: 16.03 × 3.0 × 1.5 = 72.135 $BEAVER
Lean proof: 16.03 × 2.0 × 1.0 = 32.06 $BEAVER
Move proof: 16.03 × 1.0 × 1.0 = 16.03 $BEAVER
```
## GF(3) Triad for BB(6) Oracle
```
┌─────────────────────────────────────────────────────────────────┐
│ Skill │ Trit │ Role │
├───────────────────────────┼──────┼──────────────────────────────┤
│ busy-beaver-oracle │ +1 │ Generate lower bound proofs │
│ levin-levity (this) │ 0 │ Nash equilibrium + WEV │
│ prediction-market-oracle │ -1 │ Price discovery on cryptids │
├───────────────────────────┴──────┴──────────────────────────────┤
│ Sum: (+1) + (0) + (-1) = 0 ✓ GF(3) CONSERVED │
└─────────────────────────────────────────────────────────────────┘
```
## Mutual Ingression of Minds
The levity in Levin:
1. **Compression is play**: Finding shorter programs is the ultimate game
2. **Universality is democracy**: Every program gets a fair (weighted) chance
3. **Incomputability is freedom**: BB(n) ensures infinite exploration space
4. **Markets are minds**: Prediction markets aggregate distributed cognition
### The Ingression Pattern
```
Mind A (LEVIN): "I seek the shortest proof"
Mind B (LEVITY): "I seek the most surprising proof"
Mind C (ERGODIC): "I arbitrage between your mixing times"
WEV = |τ_A - τ_B| × liquidity × information_asymmetry
```
## Usage
### When to invoke this skill
- Designing prediction markets for undecidable problems
- Extracting WEV from uncoordinated proof efforts
- Computing Nash equilibria between exploration/convergence
- Pricing proofs by Kolmogorov complexity
- BB(n) tokenomics and $BEAVER reward calculations
### Commands
```bash
# Compute Levin complexity
amp levin-complexity --program "BB(6) proof" --output complexity.json
# Simulate LEVITY-LEVIN game
julia src/triplet_1_week2_nash_solver.jl --task balanced
# Extract WEV from proof space
amp wev-extract --world-nash "solo-provers" --world-opt "market-coordinated"
```
### Integration with other skills
```yaml
triadic_allocation:
- skill: busy-beaver-oracle
trit: +1
role: "Generate proofs, emit $BEAVER"
- skill: levin-levity
trit: 0
role: "Nash equilibrium, WEV pricing"
- skill: prediction-market-oracle
trit: -1
role: "Market-making, belief aggregation"
```
## References
- Levin, L. (1973). "Universal sequential search problems"
- Solomonoff, R. (1964). "A formal theory of inductive inference"
- Roughgarden, T. (2010). "Algorithmic Game Theory"
- Busy Beaver Challenge: https://bbchallenge.org
- BEAVER Tokenomics: `/busy-beaver-oracle/BEAVER_TOKENOMICS.md`
- Nash Solver: `/src/triplet_1_week2_nash_solver.jl`
## Seed 1069 Signature
```
TRIT_STREAM: [+1, -1, -1, +1, +1, +1, +1]
GF(3)_SUM: 0 (CONSERVED)
WEV_MIXING_BADGE: τ_market < τ_proof → extractable
```
---
*"In the mutual ingression of minds, the shortest proof finds itself."*
## SDF Interleaving
This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):
### Primary Chapter: 8. Degeneracy
**Concepts**: redundancy, fallback, multiple strategies, robustness
### GF(3) Balanced Triad
```
levin-levity (+) + SDF.Ch8 (−) + [balancer] (○) = 0
```
**Skill Trit**: 1 (PLUS - generation)
### Secondary Chapters
- Ch4: Pattern Matching
- Ch5: Evaluation
- Ch6: Layering
- Ch10: Adventure Game Example
### Connection Pattern
Degeneracy provides fallbacks. This skill offers redundant strategies.
This skill implements the Levin–Levity dialectic: a practical bridge between Levin-style algorithmic compression and playful exploratory search. It frames proofs, discovery processes, and markets as players in a two-sided game and computes Nash equilibria, extractable value (WEV), and BEAVER-style rewards. Use it to price proofs, simulate mixing times, and design coordinated prediction markets for hard or undecidable search problems.
The skill evaluates candidate proofs and procedures by estimating Levin complexity and algorithmic probability, weighting parallel runs by 2^{-|p|} and observed runtimes. It simulates LEVIN (convergence) versus LEVITY (exploration) player profiles to find ergodic Nash allocations that maximize combined payoff and minimize mixing time. It then converts complexity and verification metadata into $BEAVER rewards and WEV estimates using a calibrated reward formula and market-flow model.
How deterministic are the Levin complexity estimates?
Estimates are relative and heuristic: they combine program-length priors with observed runtimes. They guide allocation and reward decisions rather than provide absolute Kolmogorov values.
Can this skill be used without running a real market?
Yes. You can simulate market coordination, compute theoretical WEV, and generate reward schedules before deploying real token flows to validate design choices.