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unwiring-arena

Play/Coplay arena theory for autopoietic closure with GF(3) conservation

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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
C3.8
Composite score
3.8
Best-practice grade
B70.4

Install command

npx @skill-hub/cli install plurigrid-asi-unwiring-arena

Repository

plurigrid/asi

Skill path: skills/unwiring-arena

Play/Coplay arena theory for autopoietic closure with GF(3) conservation

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License: Unknown.

Original source

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Repository owner: plurigrid.

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

What it helps with

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

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Original source / Raw SKILL.md

---
name: unwiring-arena
description: Play/Coplay arena theory for autopoietic closure with GF(3) conservation
version: 1.0.0
---


# Unwiring Arena Skill

**Status**: ✅ Production Ready  
**Trit**: 0 (ERGODIC - balanced flow)  
**Principle**: Play/Coplay autopoietic closure with GF(3) conservation  
**Source**: plurigrid/UnwiringDiagrams.jl#1 + Capucci et al. Arena Theory

---

## Overview

**Unwiring Arena** unifies three categorical patterns:

1. **Wiring Diagrams** (AlgebraicJulia/Catlab) - Compositional system construction
2. **Unwiring Rules** (GayUncommonsSimulator) - Learning through constraint release
3. **Arena Protocol** (Plurigrid Amelia v0.4) - Play/Coplay bidirectional channels

```
┌─ Play Channel (Outbound) ─────────────────┐
│ Local arena mutations → NATS broadcast    │
│ Strategy profiles → Action selection      │
└────────────────────────────────────────────┘
                    ↕ (Autopoietic closure)
┌─ Coplay Channel (Inbound) ───────────────┐
│ Peer arena submissions → Reconciliation   │
│ Rewards/feedback → Identity update        │
└────────────────────────────────────────────┘
```

## Mathematical Foundation

### Arenas as Parametrised Lenses

From Capucci, Ghani, Ledent, Forsberg:

```
Arena A_G : Lens_{(Ω,℧)}(X,S)(Y,R)

where:
  Ω = Π_{p∈P} Ωₚ     (strategy profiles)
  ℧ = Π_{p∈P} ℧ₚ     (reward vectors)
  X, Y = states
  S, R = costates (feedback)
```

**Play**: `play_A : Ω × X → Y` (forward pass)
**Coplay**: `coplay_A : Ω × X × R → ℧ × S` (backward pass with feedback)

### Unwiring = Learning Through Constraint Release

```julia
struct UnwiringRule
    source_gf3::Int         # Source polarity {-1, 0, +1}
    target_gf3::Int         # Target polarity
    learning_rate::Float64  # How fast to unwire
    threshold::Float64      # Discrepancy threshold to trigger
end

# Unwiring shifts internal toward external (learning)
function apply_unwiring(rule, internal, external)
    α = rule.learning_rate
    return (1-α) * internal + α * external
end
```

### GF(3) Tripartite Channels

```
MINUS (-1)   : Constraint verification (coplay focus)
ERGODIC (0)  : Balance/coordination (arena equilibrium)
PLUS (+1)    : Generative exploration (play focus)

Conservation: sum(trits) ≡ 0 (mod 3) per triplet
```

## Arena Protocol v0.4 (Amelia)

### Schema (JSONSchema v7)

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "arena_id": { "type": "string", "format": "uuid" },
    "seed": { "type": "integer", "description": "SplitMix64 seed" },
    "players": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": { "type": "string" },
          "strategy_space": { "type": "array" },
          "gf3_polarity": { "enum": [-1, 0, 1] }
        }
      }
    },
    "play_channel": {
      "type": "object",
      "properties": {
        "topic": { "type": "string", "pattern": "^plurigrid\\.arena\\.play\\." },
        "mutations": { "type": "array" }
      }
    },
    "coplay_channel": {
      "type": "object", 
      "properties": {
        "topic": { "type": "string", "pattern": "^plurigrid\\.arena\\.coplay\\." },
        "reconciliations": { "type": "array" }
      }
    }
  }
}
```

### NATS Topics

```
plurigrid.arena.play.{agent}.{focus}     # Outbound mutations
plurigrid.arena.coplay.{agent}.{focus}   # Inbound feedback
plurigrid.arena.peers.>                  # Peer discovery
plurigrid.arena.reconcile                # Conflict resolution
```

## Julia Implementation

### Core Types

```julia
using Catlab.WiringDiagrams
using Catlab.Programs

# Arena as parametrised lens
struct Arena{Ω, U, X, S, Y, R}
    play::Function      # Ω × X → Y
    coplay::Function    # Ω × X × R → U × S
    strategies::Ω
    rewards::U
end

# Unwiring rule for learning
struct UnwiringRule
    source_gf3::Int
    target_gf3::Int
    learning_rate::Float64
    threshold::Float64
end

# Agent with reafferent identity
mutable struct ArenaAgent
    id::UInt64
    internal_state::Vector{Float64}
    external_observation::Vector{Float64}
    discrepancy::Float64
    gf3::Int
    unwiring_count::Int
    current_rule::Int
end
```

### Compositional Arena Construction

```julia
using Catlab.WiringDiagrams: WiringDiagram, add_box!, add_wire!

"""
Build arena wiring diagram from player specifications.
"""
function build_arena_diagram(players::Vector{Tuple{Symbol, Int}})
    d = WiringDiagram([:X], [:Y])
    
    for (name, arity) in players
        box = add_box!(d, Box(name, fill(:strategy, arity), [:action]))
    end
    
    # Wire sequentially (can be customized)
    for i in 1:(length(players)-1)
        add_wire!(d, (i, 1) => (i+1, 1))
    end
    
    return d
end

"""
Unwire a connection (remove constraint, enable learning).
"""
function unwire!(d::WiringDiagram, wire_id::Int)
    # Remove wire - allows independent evolution
    delete_wire!(d, wire_id)
end

"""
Apply unwiring rule based on discrepancy.
"""
function apply_unwiring!(agent::ArenaAgent, rules::Vector{UnwiringRule})
    rule = rules[agent.current_rule]
    
    if agent.discrepancy > rule.threshold && agent.gf3 == rule.source_gf3
        α = rule.learning_rate
        agent.internal_state = (1-α) .* agent.internal_state .+ 
                               α .* agent.external_observation
        agent.unwiring_count += 1
        agent.current_rule = mod1(agent.current_rule + 1, length(rules))
    end
    
    update_identity!(agent)
end
```

### Play/Coplay Implementation

```julia
"""
Play: Forward pass through arena.
"""
function arena_play(arena::Arena, strategy::Ω, state::X) where {Ω, X}
    arena.play(strategy, state)
end

"""
Coplay: Backward pass with reward feedback.
"""
function arena_coplay(arena::Arena, strategy::Ω, state::X, reward::R) where {Ω, X, R}
    arena.coplay(strategy, state, reward)
end

"""
Autopoietic closure: play ∘ coplay cycle.
"""
function arena_cycle!(agent::ArenaAgent, arena::Arena, external_reward)
    # Play: agent acts
    action = arena_play(arena, agent.internal_state, agent.external_observation)
    
    # Coplay: receive feedback, update internal state
    utility, new_costate = arena_coplay(arena, agent.internal_state, 
                                         agent.external_observation, external_reward)
    
    # Update discrepancy (reafference)
    agent.external_observation = new_costate
    agent.discrepancy = norm(agent.internal_state - agent.external_observation)
    agent.gf3 = Int(hash(agent.internal_state) % 3) - 1
    
    return action, utility
end
```

## Python Implementation

```python
"""
unwiring_arena.py - Arena Protocol with Unwiring Rules
"""
from dataclasses import dataclass, field
from typing import List, Callable, Tuple, Dict, Any
import numpy as np

# SplitMix64 constants
GOLDEN = 0x9E3779B97F4A7C15
MASK64 = (1 << 64) - 1

@dataclass
class UnwiringRule:
    """How identity transforms through learning."""
    source_gf3: int      # {-1, 0, +1}
    target_gf3: int
    learning_rate: float
    threshold: float

UNWIRING_RULES = [
    UnwiringRule(0, 1, 0.1, 0.2),   # Fresh→Aging: slow learning
    UnwiringRule(1, -1, 0.3, 0.4),  # Aging→Stale: medium learning
    UnwiringRule(-1, 0, 0.5, 0.6),  # Stale→Fresh: fast learning (reset)
    UnwiringRule(0, -1, 0.05, 0.8), # Fresh→Stale: rare leap
]

@dataclass
class ArenaAgent:
    """Agent with reafferent identity."""
    id: int
    internal_state: np.ndarray
    external_observation: np.ndarray
    discrepancy: float = 0.0
    gf3: int = 0
    unwiring_count: int = 0
    current_rule: int = 0
    
    def update_identity(self):
        """Recompute identity from internal/external discrepancy."""
        self.discrepancy = np.linalg.norm(
            self.internal_state - self.external_observation
        )
        h = hash(self.internal_state.tobytes())
        self.gf3 = (h % 3) - 1

@dataclass 
class Arena:
    """Parametrised lens for game-theoretic interaction."""
    play: Callable      # (strategy, state) -> action
    coplay: Callable    # (strategy, state, reward) -> (utility, costate)
    
    def cycle(self, agent: ArenaAgent, external_reward: float) -> Tuple[Any, float]:
        """Execute play/coplay autopoietic cycle."""
        # Play: forward pass
        action = self.play(agent.internal_state, agent.external_observation)
        
        # Coplay: backward pass with feedback
        utility, new_costate = self.coplay(
            agent.internal_state, 
            agent.external_observation,
            external_reward
        )
        
        # Update agent's external observation
        agent.external_observation = new_costate
        agent.update_identity()
        
        return action, utility

def apply_unwiring(agent: ArenaAgent, rules: List[UnwiringRule] = UNWIRING_RULES):
    """Apply unwiring rule to shift internal toward external."""
    rule = rules[agent.current_rule]
    
    if agent.discrepancy > rule.threshold and agent.gf3 == rule.source_gf3:
        α = rule.learning_rate
        agent.internal_state = (
            (1 - α) * agent.internal_state + 
            α * agent.external_observation
        )
        agent.unwiring_count += 1
        agent.current_rule = (agent.current_rule + 1) % len(rules)
    
    agent.update_identity()
```

## NATS Integration

```python
import nats
import json

NATS_SERVER = "nats://nonlocal.info:4222"

async def publish_arena_play(nc, agent_id: str, focus: str, mutation: dict):
    """Publish play channel mutation."""
    topic = f"plurigrid.arena.play.{agent_id}.{focus}"
    await nc.publish(topic, json.dumps(mutation).encode())

async def subscribe_arena_coplay(nc, agent_id: str, handler):
    """Subscribe to coplay channel feedback."""
    topic = f"plurigrid.arena.coplay.{agent_id}.>"
    await nc.subscribe(topic, cb=handler)

async def broadcast_reconciliation(nc, arena_state: dict):
    """Broadcast arena state for reconciliation."""
    await nc.publish("plurigrid.arena.reconcile", json.dumps(arena_state).encode())
```

## Integration with GayUncommonsSimulator

```julia
# From GayUncommonsSimulator.jl - the key insight
# Wiring = constraint (commons pooling)
# Unwiring = freedom (uncommons differentiation)

const UNWIRING_RULES = [
    UnwiringRule(0, 1, 0.1, 0.2),  # Fresh→Aging
    UnwiringRule(1, 2, 0.3, 0.4),  # Aging→Stale
    UnwiringRule(2, 0, 0.5, 0.6),  # Stale→Fresh (reset cycle)
]

# Identity IS discrepancy
struct ReafferentID
    internal::RGB
    external::RGB
    discrepancy::Float64
    hash::UInt64
    gf3::Int
end

# ID = hash(internal ⊻ external)
function ReafferentID(internal::RGB, external::RGB)
    disc = abs(spectrum_t(internal) - spectrum_t(external))
    id_hash = mix(to_hash(internal) ⊻ to_hash(external))
    gf3 = Int(id_hash % 3)
    ReafferentID(internal, external, disc, id_hash, gf3)
end
```

## Chronoscope Video Integration

The Arena Protocol includes video orchestration for 69 demonstrations:

```julia
# 7 categories × ~10 videos each
CHRONOSCOPE_CATEGORIES = [
    :categorical_ops,      # Operad operations
    :quantum,              # ZX calculus
    :hypergraph,           # Hypergraph rewriting
    :ducklake,             # Time-travel queries
    :embodied_gradualism,  # Learning dynamics
    :ontology,             # Schema evolution
    :arena_protocol,       # This very protocol
]

# Deterministic scheduling with seed 1069
schedule_videos(seed=1069, categories=CHRONOSCOPE_CATEGORIES)
```

## Commands

```bash
# Julia - run arena simulation
julia -e "include(\"GayUncommonsSimulator.jl\"); simulate_wev(n_steps=100)"

# Python - NATS arena client
uv run --with nats-py arena_client.py

# Catlab wiring diagram
julia -e "using Catlab.WiringDiagrams; d = WiringDiagram([:X], [:Y])"
```

## Key Patterns

| Pattern | Wiring | Unwiring |
|---------|--------|----------|
| **Semantics** | Constraint | Freedom |
| **Direction** | Composition | Decomposition |
| **Learning** | Fixed relationship | Adaptive relationship |
| **GF3 Flow** | -1 (MINUS) | +1 (PLUS) |

## See Also

- [GayUncommonsSimulator.jl](file:///Users/bob/ies/GayUncommonsSimulator.jl) - Full implementation
- [plurigrid/UnwiringDiagrams.jl#1](https://github.com/plurigrid/UnwiringDiagrams.jl/pull/1) - Arena Protocol PR
- [Capucci et al.](file:///Users/bob/ies/paper_extracts/mathpix_snips/obsidian/math/M.%20Capucci,%20N.%20Ghani,%20J.%20Ledent,%20F.%20Nordvall%20Forsberg.md) - Arenas with players
- [gay-mcp](file:///Users/bob/.claude/skills/gay-mcp/SKILL.md) - Deterministic colors
- [triad-interleave](file:///Users/bob/.claude/skills/triad-interleave/SKILL.md) - GF(3) scheduling

## Thread History (Extracted Patterns)

### Core Arena/Games Threads

| Thread | Messages | Key Pattern |
|--------|----------|-------------|
| [GayUncommonsSimulator with reafference](https://ampcode.com/threads/T-019b22bf-11fd-73ad-a61a-e7c40373fd49) | 61 | ID = hash(internal ⊻ external), WEV extraction |
| [GAY protocol stack + open games](https://ampcode.com/threads/T-019b22cd-ef86-77eb-a285-8dbe2312558f) | 39 | 3-coloring guarantees, Hedges open games |
| [DiscoHy operadic framework](https://ampcode.com/threads/T-019b44e5-39bb-7251-a8fa-5d1efaa5dafa) | 55 | ParaLens, Hedges equilibria, operads |
| [Random walk spectral gap](https://ampcode.com/threads/T-019b43de-907c-7008-a545-57e8ff698498) | 104 | Open-games + active-inference GF(3) balance |

### Wiring/Category Threads

| Thread | Messages | Key Pattern |
|--------|----------|-------------|
| [Load acset skill](https://ampcode.com/threads/T-019b43a6-11f7-77a8-a9cd-ea618e457b70) | 88 | C-set as functor, DPO rewriting |
| [Formalize skills with interaction entropy](https://ampcode.com/threads/T-019b44d2-05db-72e2-9a4a-23b7e1085b25) | 61 | DisCoPy boxes, ACSet integration |
| [Install plurigrid ASI skills](https://ampcode.com/threads/T-019b44c6-bfa3-7371-b238-023eb308d12b) | 124 | SchOperadNetwork, morphism traversal |
| [P2P file exchange + skills](https://ampcode.com/threads/T-019b4464-a75b-714e-9f73-e4e1dfd82ab7) | 162 | DiscoPy operads, 1069 subscales |

### WEV/Value Extraction Threads

| Thread | Messages | Key Pattern |
|--------|----------|-------------|
| [Comprehensive GAY protocol](https://ampcode.com/threads/T-019b22d0-c29f-72e7-a229-57a99774d3fa) | 160 | 3-match guarantee, pigeonhole bound |
| [Find recent fork threads](https://ampcode.com/threads/T-019b22aa-e7ef-779c-9246-9977c1c6a9ae) | 150 | WEV formula, colored operad schema |
| [Self-reflexive triadic subagent](https://ampcode.com/threads/T-019b233a-7ddf-734c-b117-2dd65ae97a80) | 141 | Tripartite network, GF(3) conservation |

### Key Extracted Formulas

```julia
# WEV (World Extractable Value)
WEV(agent, world) = base_value × staleness_mult × scarcity_mult

where:
  base_value     = agent.discrepancy × 10.0
  staleness_mult = 1.0 + Σ(days_since_asked) × 0.1
  scarcity_mult  = 1.0 + (1.0 - occupancy[color] / total)

# 3-Coloring Guarantees
∀ i: color[i] ≠ color[i+1]           # Adjacent Distinct
∀ window of 3: {R, Y, B} all present  # 3-Match Property

# Reafferent Identity
ID = hash(internal_color ⊻ external_color)
discrepancy = |spectrum_t(internal) - spectrum_t(external)|
```

### ACSet Schema for Arena Protocol

```julia
@present SchArenaProtocol(FreeSchema) begin
    Agent::Ob
    Arena::Ob
    Channel::Ob
    Message::Ob
    
    agent_arena::Hom(Agent, Arena)
    msg_channel::Hom(Message, Channel)
    channel_arena::Hom(Channel, Arena)
    
    # Attributes
    Name::AttrType
    Trit::AttrType
    Seed::AttrType
    
    agent_name::Attr(Agent, Name)
    agent_trit::Attr(Agent, Trit)
    arena_seed::Attr(Arena, Seed)
    channel_type::Attr(Channel, Name)  # "play" or "coplay"
end
```

---

**Skill Name**: unwiring-arena  
**Type**: Compositional Game Theory / Learning  
**Trit**: 0 (ERGODIC)  
**GF(3)**: Play/Coplay closure maintains conservation  
**SPI**: Deterministic arena fingerprints via seed chain
**Threads**: 15+ related threads with 1400+ combined messages



## Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

### Graph Theory
- **networkx** [○] via bicomodule
  - Universal graph hub

### Bibliography References

- `general`: 734 citations in bib.duckdb

## Cat# Integration

This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:

```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```

### GF(3) Naturality

The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```

This ensures compositional coherence in the Cat# equipment structure.
unwiring-arena | SkillHub