Parametrised Optics Cybernetics

Parametrised Optics for Cybernetic Systems

Status: Production Ready Trit: 0 (ERGODIC - mediates agent↔system) Color: #25D09C

"Parametrised optics model cybernetic systems, namely dynamical systems steered by one or more agents. Then ⊛ represents agency being exerted on systems." — Capucci, Gavranović et al.

Core Structure

The ⊛ Action (Agency)

        ┌─────────────────────────────────────┐
        │         CYBERNETIC SYSTEM           │
        │                                     │
   P ───┼──⊛──▶ S ────────▶ S'               │
(params)│      (state)    (next state)        │
        │        │                            │
        │        ▼                            │
        │    observation ◀── environment      │
        │        │                            │
        │        ▼                            │
        │   P' ◀─┘ (updated params)           │
        └─────────────────────────────────────┘

The actegory action ⊛ : P × S → S means:

• P (parameters/policy) = agent's controllable degrees of freedom
• S (state) = system being controlled
• ⊛ = agency exerted: parameters steer the system

Para Construction

-- Para(C) is the category of parametrised morphisms in C
data Para c a b where
  Para :: c p () -> c (p, a) b -> Para c a b

-- Composition via tensor product of parameters
(>>>) :: Para c a b -> Para c b d -> Para c a d
Para p f >>> Para q g = Para (p ⊗ q) (f ; (id ⊗ g))

In Poly (polynomial functors):

Para(Poly) ≅ Lens    -- parametrised polynomial = lens

Lens as Bidirectional Control

        get
    S ────────▶ A        (observation)
    │           │
    │    set    │
    ◀───────────┘        (action)
    S'    ◀── (S, B)

A lens Lens S A has:

• get : S → A — observe part of state
• set : S × A → S — update state with new value

A parametrised lens PLens P S A:

• get : P × S → A — observation depends on parameters
• set : P × S × A → S — action depends on parameters

Connection to Powers PCT

The hierarchical control from Powers maps directly:

PCT as Parametrised Optic

-- Perceptual Control Theory as Para
pctController :: Para C Reference Perception
pctController = Para policy controlLoop
  where
    controlLoop (ref, percept) =
      let error = ref - percept
          action = gain * error
      in applyAction action percept

Open Games Connection

Open games (Hedges) are parametrised optics for strategic interaction:

       ┌─────────────────┐
   X ──┤                 ├──▶ Y     (play)
       │   Open Game G   │
   R ◀─┤                 │◀── S     (coutility)
       └─────────────────┘

An open game G : (X, S) → (Y, R) has:

• Play: X → Y (forward: strategy → outcome)
• Coplay: X × S → R (backward: utility propagation)

This is exactly a parametrised optic where:

• Parameters = strategies
• ⊛ = strategic interaction
• Backward pass = utility/gradient flow

Nash Equilibria via Optics

-- Nash equilibrium = fixed point of best response
nashEquilibrium :: OpenGame X S Y R -> X -> Bool
nashEquilibrium game strategy =
  bestResponse game strategy == strategy

ALIEN Screenshot Interpretation

The ALIEN simulation shows this structure:

┌─────────────────────────────────────────────────────────┐
│  NEURAL TOPOLOGY (center)     │  MATRIX RAIN (right)    │
│  ════════════════════════     │  ═══════════════════    │
│                               │                         │
│  ●───●───●───●                │  Activity transcript    │
│  │ ╲ │ ╱ │ ╲ │                │  = feedback signals     │
│  ●───●───●───●   ◀──P (params)│  = utility gradients    │
│  │ ╱ │ ╲ │ ╱ │                │  = error propagation    │
│  ●───●───●───●                │                         │
│       │                       │                         │
│       ▼                       │                         │
│   S (state) ──⊛──▶ S'         │                         │
│                               │                         │
└─────────────────────────────────────────────────────────┘

• Neural nodes = parametrised lenses into creature state
• Connections = composition of optics
• Matrix rain = backward pass (coplay/utility/gradients)
• ⊛ action = creatures exerting agency on environment

Formal Definition

Parametrised Optic

Given a monoidal category (M, ⊗, I) acting on C via ⊛:

ParaOptic M C (S, S') (A, A') = ∫^P M(I, P) × C(P ⊛ S, A) × C(P ⊛ A', S')

This coend captures:

• Parameters P from the monoidal category M
• Forward map P ⊛ S → A (observation under parameters)
• Backward map P ⊛ A' → S' (update under parameters)

Actegory

An actegory is a category C with an action ⊛ : M × C → C satisfying:

• I ⊛ X ≅ X (identity acts trivially)
• (P ⊗ Q) ⊛ X ≅ P ⊛ (Q ⊛ X) (associativity)

Agency = choosing P to steer X.

GF(3) Triads

open-games (-1) ⊗ parametrised-optics (0) ⊗ gay-mcp (+1) = 0 ✓
powers-pct (-1) ⊗ parametrised-optics (0) ⊗ alife (+1) = 0 ✓
lens-bidirectional (-1) ⊗ parametrised-optics (0) ⊗ active-inference (+1) = 0 ✓

Code Examples

Haskell (Optics)

{-# LANGUAGE GADTs #-}

-- Parametrised lens
data PLens p s a = PLens
  { pget :: p -> s -> a
  , pset :: p -> s -> a -> s
  }

-- Composition
(|.|) :: PLens p s a -> PLens p a b -> PLens p s b
(PLens g1 s1) |.| (PLens g2 s2) = PLens
  { pget = \p s -> g2 p (g1 p s)
  , pset = \p s b -> s1 p s (s2 p (g1 p s) b)
  }

-- Cybernetic control loop
controlLoop :: PLens Policy State Observation -> State -> State
controlLoop lens state =
  let obs = pget lens policy state
      err = reference - obs
      action = gain * err
  in pset lens policy state (obs + action)

Julia (ACSets)

using Catlab, Catlab.CategoricalAlgebra

# Schema for parametrised optic
@present SchParaOptic(FreeSchema) begin
  Param::Ob
  State::Ob
  Obs::Ob

  # Forward: P ⊛ S → A
  forward::Hom(Param ⊗ State, Obs)

  # Backward: P ⊛ A' → S'
  backward::Hom(Param ⊗ Obs, State)

  # Action composition
  compose::Hom(Param ⊗ Param, Param)
end

@acset_type ParaOptic(SchParaOptic)

Python (Open Games)

from dataclasses import dataclass
from typing import Callable, TypeVar

P, S, A, R = TypeVar('P'), TypeVar('S'), TypeVar('A'), TypeVar('R')

@dataclass
class OpenGame:
    """Parametrised optic for strategic interaction."""
    play: Callable[[P, S], A]      # Forward: strategy → outcome
    coplay: Callable[[P, S, R], R]  # Backward: utility propagation

    def compose(self, other: 'OpenGame') -> 'OpenGame':
        """Sequential composition via ⊛."""
        return OpenGame(
            play=lambda p, s: other.play(p, self.play(p, s)),
            coplay=lambda p, s, r: self.coplay(p, s, other.coplay(p, self.play(p, s), r))
        )

    def tensor(self, other: 'OpenGame') -> 'OpenGame':
        """Parallel composition via ⊗."""
        return OpenGame(
            play=lambda p, s: (self.play(p[0], s[0]), other.play(p[1], s[1])),
            coplay=lambda p, s, r: (self.coplay(p[0], s[0], r[0]),
                                     other.coplay(p[1], s[1], r[1]))
        )

Integration with Gay.jl

using Gay

# Seed for cybernetic color palette
gay_seed!(0xCYBER)

# Hierarchical control colors
CYBERNETIC_COLORS = Dict(
    :agent     => color_at(1),  # Parameters
    :system    => color_at(2),  # State
    :forward   => color_at(3),  # Observation
    :backward  => color_at(4),  # Utility/gradient
)

# ⊛ action visualization
function visualize_agency(params, state, action)
    p_color = CYBERNETIC_COLORS[:agent]
    s_color = CYBERNETIC_COLORS[:system]
    # Blend represents ⊛ application
    result_color = blend(p_color, s_color, action.intensity)
    return result_color
end

References

1. Capucci, M. - "Seeing Double Through Dependent Optics"
2. Gavranović, B. - "Categorical Foundations of Gradient-Based Learning"
3. Hedges, J. - "Compositionality and String Diagrams for Game Theory"
4. Powers, W.T. - "Behavior: The Control of Perception" (1973)
5. Riley, M. - "Categories of Optics"
6. Spivak, D.I. - "Poly: An Abundant Categorical Setting"

Related Skills

Skill Name: parametrised-optics-cybernetics Type: Categorical Framework / Cybernetics Trit: 0 (ERGODIC - bridges agent and system) Key Operator: ⊛ (actegory action = agency) Structure: Para(C) with lenses and open games

Autopoietic Marginalia

The interaction IS the skill improving itself.

Every use of this skill is an opportunity for worlding:

• MEMORY (-1): Record what was learned
• REMEMBERING (0): Connect patterns to other skills
• WORLDING (+1): Evolve the skill based on use

Add Interaction Exemplars here as the skill is used.

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