Back to skills
SkillHub ClubShip Full StackFull Stack

epistemic-arbitrage

Propagator-based parallel structure for exploiting knowledge differentials across domains using local scoped propagators and SplitMixTernary RNG.

Packaged view

This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.

Stars
10
Hot score
84
Updated
March 20, 2026
Overall rating
C3.9
Composite score
3.9
Best-practice grade
B77.6

Install command

npx @skill-hub/cli install plurigrid-asi-epistemic-arbitrage

Repository

plurigrid/asi

Skill path: ies/music-topos/.ruler/skills/epistemic-arbitrage

Propagator-based parallel structure for exploiting knowledge differentials across domains using local scoped propagators and SplitMixTernary RNG.

Open repository

Best for

Primary workflow: Ship Full Stack.

Technical facets: Full Stack.

Target audience: everyone.

License: MIT.

Original source

Catalog source: SkillHub Club.

Repository owner: plurigrid.

This is still a mirrored public skill entry. Review the repository before installing into production workflows.

What it helps with

  • Install epistemic-arbitrage into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
  • Review https://github.com/plurigrid/asi before adding epistemic-arbitrage to shared team environments
  • Use epistemic-arbitrage for development workflows

Works across

Claude CodeCodex CLIGemini CLIOpenCode

Favorites: 0.

Sub-skills: 0.

Aggregator: No.

Original source / Raw SKILL.md

---
name: epistemic-arbitrage
description: Propagator-based parallel structure for exploiting knowledge differentials across domains using local scoped propagators and SplitMixTernary RNG.
source: music-topos/skills
license: MIT
---

# Epistemic Arbitrage: Propagator-Based Knowledge Synthesis

Epistemic arbitrage exploits **knowledge differentials** between domains. When concept A in domain X is well-understood, but its isomorphic counterpart A' in domain Y is mysterious, we can **arbitrage** by transferring understanding.

## Core Architecture

### Propagator Networks (Radul/Sussman)

A propagator network consists of:

```
┌─────────┐     ┌─────────────┐     ┌─────────┐
│  Cell A │────▶│ Propagator  │────▶│  Cell B │
│ (known) │     │  (transfer) │     │(derived)│
└─────────┘     └─────────────┘     └─────────┘
```

- **Cells**: Containers for partial information
- **Propagators**: Functions that combine/transform information
- **Merging**: Monotonic accumulation (information only increases)

### SplitMixTernary RNG

For parallel propagation with determinism:

```ruby
class SplitMixTernary
  GOLDEN = 0x9e3779b97f4a7c15  # Golden ratio constant
  
  def initialize(seed)
    @state = seed
  end
  
  def next_ternary
    # Advance state (SplitMix64)
    @state = (@state + GOLDEN) & 0xFFFFFFFFFFFFFFFF
    z = @state
    z = ((z ^ (z >> 30)) * 0xbf58476d1ce4e5b9) & 0xFFFFFFFFFFFFFFFF
    z = ((z ^ (z >> 27)) * 0x94d049bb133111eb) & 0xFFFFFFFFFFFFFFFF
    z = z ^ (z >> 31)
    
    # Map to ternary: -1, 0, +1
    (z % 3) - 1
  end
  
  def split(index)
    # Create independent child stream
    child_seed = @state ^ (index * GOLDEN)
    SplitMixTernary.new(child_seed)
  end
end
```

## Arbitrage Patterns

### Pattern 1: Domain Transfer

```ruby
# Knowledge in domain A
cell_a = Cell.new(:music, :circle_of_fifths)
cell_a.add_info(:structure, :cyclic_group_12)
cell_a.add_info(:generator, :perfect_fifth)

# Unknown in domain B
cell_b = Cell.new(:number_theory, :modular_arithmetic)

# Propagator transfers structure
propagator = Propagator.new(:domain_transfer) do |source, target|
  if source.has?(:structure)
    target.merge(:structure, source.get(:structure))
    target.merge(:insight, "Z/12Z isomorphic to circle of fifths")
  end
end

propagator.connect(cell_a, cell_b)
propagator.fire!
```

### Pattern 2: Dual Discovery

```ruby
# Known: Fourier transform on functions
cell_time = Cell.new(:analysis, :time_domain)
cell_freq = Cell.new(:analysis, :frequency_domain)

# Propagator: Fourier duality
duality_propagator = Propagator.new(:fourier_duality) do |time, freq|
  time.each_property do |prop, val|
    dual_prop = fourier_dual(prop)  # convolution ↔ multiplication, etc.
    freq.merge(dual_prop, val)
  end
end
```

### Pattern 3: Triangle Arbitrage

When three domains have pairwise connections, exploit the triangle:

```ruby
# Three domains with partial knowledge
cell_math = Cell.new(:mathematics, :group_theory)
cell_music = Cell.new(:music, :harmony)
cell_physics = Cell.new(:physics, :symmetry)

# Pairwise propagators
math_music = Propagator.new(:pitch_class_groups)
music_physics = Propagator.new(:overtone_series)
physics_math = Propagator.new(:lie_groups)

# Triangle inequality enables arbitrage:
# d(math, physics) ≤ d(math, music) + d(music, physics)
# 
# If direct math→physics is hard, go via music!
```

## Local Scoped Propagators

### Scoping for Parallelism

Each propagator runs in a local scope with:

```ruby
class ScopedPropagator
  def initialize(name, rng_seed, &block)
    @name = name
    @rng = SplitMixTernary.new(rng_seed)
    @block = block
    @local_cells = {}
  end
  
  def fork(n)
    # Create n parallel scopes with independent RNG streams
    n.times.map do |i|
      child_rng = @rng.split(i)
      ScopedPropagator.new("#{@name}/#{i}", child_rng.state, &@block)
    end
  end
  
  def run(inputs)
    # Execute in isolated scope
    @local_cells = inputs.dup
    @block.call(self, @local_cells)
    @local_cells
  end
end
```

### Parallel Arbitrage Network

```ruby
# Master network
network = ArbitrageNetwork.new(seed: 1069)

# Add cells for each domain
network.add_cell(:category_theory, knowledge: 0.9)
network.add_cell(:music_theory, knowledge: 0.7)
network.add_cell(:physics, knowledge: 0.5)
network.add_cell(:philosophy, knowledge: 0.3)

# Add arbitrage propagators
network.add_propagator(:category_to_music, [:category_theory], [:music_theory])
network.add_propagator(:music_to_physics, [:music_theory], [:physics])
network.add_propagator(:physics_to_philosophy, [:physics], [:philosophy])

# Run in parallel (SPI-compliant)
results = network.run_parallel(n_workers: 4)
```

## Integration with Music Topos

### With World Broadcast

```ruby
# Each mathematician is a knowledge source
broadcasters = WorldBroadcast::TripartiteSystem.new([:ramanujan, :grothendieck, :euler])

# Create cells from mathematician expertise
cells = broadcasters.agents.map do |agent|
  Cell.new(agent.profile[:domain], 
           knowledge: agent.history.size,
           operations: agent.profile[:operations])
end

# Arbitrage between mathematicians
network = ArbitrageNetwork.from_cells(cells)
network.add_all_pairwise_propagators
network.run!
```

### With Synadia

```ruby
# Publish arbitrage opportunities
network.on_arbitrage do |source, target, gain|
  SynadiaBroadcast.publish("arbitrage.opportunity", {
    from: source.domain,
    to: target.domain,
    knowledge_gain: gain
  })
end

# Subscribe to external knowledge
SynadiaBroadcast.subscribe("knowledge.*") do |msg|
  domain = msg.subject.split('.').last.to_sym
  network.cells[domain].merge(:external, msg.data)
end
```

### With Glass Bead Game

```ruby
# Arbitrage opportunities become game moves
network.on_arbitrage do |source, target, gain|
  if gain > threshold
    move = GlassBeadGame::Connect.new(
      from: Bead.new(source.domain, source.best_concept),
      to: Bead.new(target.domain, target.mystery),
      via: :epistemic_transfer,
      value: gain
    )
    game.propose_move(move)
  end
end
```

## Guarantees

1. **SPI Compliance**: Parallel execution ≡ sequential (bitwise identical)
2. **Determinism**: Same seed → same arbitrage discoveries
3. **Monotonicity**: Knowledge only increases (no forgetting)
4. **Locality**: Propagators only access explicitly connected cells
5. **Triangle Inequality**: d(A,C) ≤ d(A,B) + d(B,C) for all knowledge transfers

## Commands

```bash
just arbitrage               # Run arbitrage network
just arbitrage-domains a b   # Arbitrage between specific domains
just propagator-network      # Visualize propagator network
just knowledge-flow          # Show knowledge flow diagram
```