What is the difference between Play and Coplay?

Play represents the forward pass or outbound action selection, while Coplay represents the backward pass where inbound feedback and rewards update the system's identity.

What is an Unwiring Arena?

It is a framework for modeling systems that learn and evolve by strategically releasing constraints (unwiring) through bidirectional interaction channels called Arenas.

How does GF(3) conservation work in this skill?

It uses three polarities—Minus (Constraint), Ergodic (Balance), and Plus (Generative)—ensuring that the sum of trits in a system triplet remains conserved (mod 3).

Is this skill compatible with Julia and Python?

Yes, it provides implementation patterns for both Julia (using Catlab.jl) and Python (using NumPy and NATS) to ensure cross-language systems modeling.

Can I use this for multi-agent systems?

Yes, the skill includes a NATS-integrated protocol for agent discovery, mutation broadcasting, and conflict reconciliation in decentralized environments.

Unwiring Arena

Name: Unwiring Arena
Author: plurigrid

byplurigrid

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数据科学与机器学习

Models and simulates autopoietic systems using arena theory and GF(3) conservation rules for adaptive learning.

关于

The Unwiring Arena skill implements the Arena Protocol (Amelia v0.4), a sophisticated framework for modeling complex system interactions as bidirectional 'Play' and 'Coplay' channels. Based on the mathematics of parametrised lenses and categorical wiring diagrams, it enables developers to simulate learning through 'unwiring'—the strategic release of constraints based on state discrepancy. This skill is ideal for building self-organizing systems, multi-agent game-theoretic environments, and topological computing models that require formal conservation laws and compositional construction.

主要功能

Provides unwiring logic for adaptive learning and constraint release
Applies GF(3) conservation rules for tripartite system balancing
Supports compositional system construction via Catlab-style wiring diagrams
2 GitHub stars
Integrates with NATS for distributed agent coordination and reconciliation
Implements Play/Coplay bidirectional channels for autopoietic closure

使用场景

Simulating learning dynamics in complex categorical systems
Designing multi-agent environments with self-organizing identities
Building decentralized game-theoretic protocols with formal feedback loops

关于

主要功能

Provides unwiring logic for adaptive learning and constraint release
Applies GF(3) conservation rules for tripartite system balancing
Supports compositional system construction via Catlab-style wiring diagrams
2 GitHub stars
Integrates with NATS for distributed agent coordination and reconciliation
Implements Play/Coplay bidirectional channels for autopoietic closure

使用场景

Simulating learning dynamics in complex categorical systems
Designing multi-agent environments with self-organizing identities
Building decentralized game-theoretic protocols with formal feedback loops