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Behaviour Surprisal Analysis

Behaviour Surprisal Analysis

Installable skillMITBy plurigridUpdated Oct 30, 2026
INTENDED FORClaude CodeInstall in a listed agent.

instructions.md

---
name: behaviour-surprisal-analysis-773039c936e0
description: Behaviour Surprisal Analysis
version: 1.0.0
---

# Behaviour Surprisal Analysis

**Status**: Production Ready (v3.0 - Cat# Integration)
**Trit**: 0 (ERGODIC - measurement/observation)
**Principle**: S(x) = -log₂(P(x|attention_mode))
**Frame**: Tri-channel prediction evaluation with AGM belief revision + Cat# bicomodule structure

---

## Overview

**Behaviour Surprisal Analysis** calculates information-theoretic surprise between predictions and observed outcomes using three complementary attention channels mapped to Cat# = Comod(P) structure:

| Channel | Trit | Home | Poly Op | Kan Role | Description |
|---------|------|------|---------|----------|-------------|
| **Direct** (α) | −1 | Span | × (product) | Ran_K | Exact artifact matching |
| **Diffuse** (β) | 0 | Prof | ⊗ (parallel) | Adj | Thematic/structural matching |
| **Meta** (γ) | +1 | Presheaves | ◁ (substitution) | Lan_K | Capability/infrastructure tracking |

```
Total Surprisal = α·S_direct + β·S_diffuse + γ·S_meta
where α + β + γ = 1 and typically α=0.3, β=0.5, γ=0.2
```

## Cat# Integration (v3.0)

### Galois Adjunction α ⊣ γ

The Direct and Meta channels form a Galois adjunction through the Diffuse bridge:

```
         α (abstract)
  Direct ─────────────→ Diffuse
    ↑                      │
    │        CatSharp      │ γ (concretize)
    │         Scale        │
    └──────────────────────┘
           Meta

  GF(3): (−1) + (0) + (+1) = 0 ✓
```

- **α (abstraction)**: Direct predictions → Diffuse patterns
- **γ (concretization)**: Diffuse patterns → Direct predictions
- **Unit η**: id → γ∘α verifies coherence

### Three Homes (Spivak ACT 2023)

Each channel lives in a specific Cat# home:

```
┌────────────┬─────────────┬──────────┬───────────────┬────────────┐
│  Channel   │  Poly Op    │ Kan Role │   Structure   │   Home     │
├────────────┼─────────────┼──────────┼───────────────┼────────────┤
│  Direct    │  × (prod)   │  Ran_K   │ cofree t_p    │   Span     │
│  Diffuse   │  ⊗ (para)   │  Adj     │ bicomodule    │   Prof     │
│  Meta      │  ◁ (subst)  │  Lan_K   │ free m_p      │ Presheaves │
└────────────┴─────────────┴──────────┴───────────────┴────────────┘
```

### Bicomodule Coherence

Predictions and observations form bicomodule pairs. Coherence is verified by:

1. **Galois unit check**: η: id → γ∘α preserves trits
2. **Bicomodule compatibility**: pred_trit ↔ obs_trit compatible homes

## CatSharp Scale Sonification

Surprisal values map to pitch classes via the CatSharp scale:

| Trit | Pitch Classes | Chord Type | Hz Range |
|------|---------------|------------|----------|
| +1 (PLUS) | {0, 4, 8} | Augmented triad | C-E-G# |
| 0 (ERGODIC) | {3, 6, 9} | Diminished 7th | D#-F#-A-C |
| −1 (MINUS) | {1,2,5,7,10,11} | Fifths cycle | C#,D,F,G,A#,B |

```clojure
;; Surprisal → Pitch class → Frequency
(defn surprisal->pitch-class [surp]
  (mod (Math/round (* (min surp 10.0) 1.2)) 12))

(defn pitch-class->freq [pc]
  (* 261.63 (Math/pow 2 (/ pc 12.0))))  ;; C4 = 261.63 Hz
```

Enable with `--sonify` flag to hear the surprisal as tones via sox.

## AGM Belief Revision (Levi Identity)

Based on [Baker 2023](https://ijcai.org/proceedings/2023/811):

```
K * φ = (K − ¬φ) + φ   (Levi Identity)
```

- **Contraction (K − ¬φ)**: Remove predictions contradicted by observations
- **Expansion (+ φ)**: Add new beliefs from observed data
- **Revision (K * φ)**: Combined operation via Levi identity

### Spohn κ-Ranking

Predictions ranked by entrenchment:

```clojure
(defn kappa-rank [belief]
  (- (Math/log (/ 1 (max 0.01 (:confidence belief))))))
```

Lower κ = more entrenched = harder to revise.

## Usage

```bash
# Full Cat# analysis with sonification
bb ~/.claude/skills/behaviour-surprisal-analysis/analyse.bb \
  --predictions predictions.json \
  --observed observed.json \
  --alpha 0.3 --beta 0.5 --gamma 0.2 \
  --sonify

# With capability tracking
bb analyse.bb \
  --predictions predictions.json \
  --observed observed.json \
  --skills-before skills_t0.txt \
  --skills-after skills_t30.txt

# Direct-heavy (Span home focus)
bb analyse.bb --alpha 0.7 --beta 0.2 --gamma 0.1

# Meta-heavy (Presheaves home focus)
bb analyse.bb --alpha 0.1 --beta 0.3 --gamma 0.6 --sonify
```

## Input Format

```json
{
  "predictions": {
    "direct": [
      {"content": "Ruby MCP SDK for skill markets", "confidence": 0.8},
      {"content": "VirtualizationBridge sandbox test", "confidence": 0.7}
    ],
    "diffuse": [
      {"theme": "GF(3) conservation", "keywords": ["trit", "lattice", "conservation"]},
      {"theme": "skill markets", "keywords": ["confidential", "commitment", "beacon"]}
    ],
    "meta": {
      "skills_before": 45,
      "mcp_servers_before": 12,
      "config_hash": "a3f2c1"
    }
  },
  "observed": {
    "threads": [
      "Ruby MCP SDK for confidential skill markets",
      "GF(3) skill composition and Galois connection verification",
      "Derangement operators and GF(3) entropy management"
    ],
    "capability_events": [
      {"type": "skill_install", "count": 373, "source": "plurigrid/asi"},
      {"type": "mcp_addition", "server": "world_a_aptos"}
    ]
  }
}
```

## Output Format

```
╔══════════════════════════════════════════════════════════════════╗
║  BEHAVIOUR SURPRISAL ANALYSIS v3.0 (Cat# + AGM)                  ║
║  α=0.30 (Span/Ran) β=0.50 (Prof/Adj) γ=0.20 (Presh/Lan)          ║
╚══════════════════════════════════════════════════════════════════╝

  DIRECT ATTENTION (Home: Span, Kan: Ran_K)
  ───────────────────────────────────────────────────────────────
  Prediction                      │ Match │ S_dir │ Trit │ PC │ Home
  ────────────────────────────────┼───────┼───────┼──────┼────┼─────
  VirtualizationBridge sandbox    │  34.9% │  1.52 │  +   │  2 │ Span
  ...

  CAT# COHERENCE
  ───────────────────────────────────────────────────────────────
  Galois adjunction α ⊣ γ:    ✓ coherent
  Bicomodule compatibility:   85.0% (✓)

  CATSHARP SONIFICATION
  ───────────────────────────────────────────────────────────────
  ♪ Direct (Ran_K): 293.7 Hz
  ♪ Diffuse (Adj): 329.6 Hz
  ♪ Meta (Lan_K): 261.6 Hz
```

## GF(3) Triads

The skill participates in balanced triads:

```
behaviour-surprisal-analysis (0) ⊗ catsharp-galois (0) ⊗ gay-mcp (-1) + operad-compose (+1) = 0 ✓

# Internal channel triad
Direct (−1) + Diffuse (0) + Meta (+1) = 0 ✓
```

## Attention Calibration

| Prediction Style | Recommended (α,β,γ) | Cat# Focus |
|-----------------|---------------------|------------|
| Specific artifacts | (0.6, 0.3, 0.1) | Span heavy |
| Thematic directions | (0.2, 0.6, 0.2) | Prof heavy |
| Capability exploration | (0.2, 0.3, 0.5) | Presheaves heavy |
| Mixed/balanced | (0.3, 0.5, 0.2) | Bicomodule equilibrium |

## API

```clojure
(require '[behaviour-surprisal-analysis :as bsa])

;; Full Cat# analysis
(bsa/combined-analysis
  predictions observed
  0.3 0.5 0.2          ;; α β γ
  before-state after-state
  capability-events
  true)                ;; sonify?

;; Galois adjunction verification
(bsa/verify-galois-unit direct-result)

;; Bicomodule coherence check
(bsa/check-bicomodule-coherence direct diffuse meta)

;; Sonify channel
(bsa/sonify-channel results "Direct" 0.3)
```

## Philosophical Foundation

The tri-channel Cat# model reflects:

1. **Cat# Three Homes**: Span (comodules), Prof (bimodules), Presheaves (right modules)
2. **Kan Extensions**: Ran_K (limit/consume), Lan_K (colimit/generate), Adj (bridge)
3. **Galois Adjunction**: α ⊣ γ for abstraction/concretization
4. **AGM Epistemology**: Contraction, Expansion, Revision via Levi identity
5. **CatSharp Scale**: Mazzola's categorical music theory for sonification

### Key Insight: GF(3) = Naturality

GF(3) conservation IS the naturality condition of Cat# equipment:

```
For a triad (s₋₁, s₀, s₊₁):
  Ran_K(s₋₁) →[bicomodule]→ s₀ →[bicomodule]→ Lan_K(s₊₁)
  
  The commuting square:
    G(f) ∘ η_A = η_B ∘ F(f)
    
  Becomes the GF(3) equation:
    (−1) + (0) + (+1) ≡ 0 (mod 3)
```

---

**Skill Name**: behaviour-surprisal-analysis
**Version**: 3.0.0 (Cat# Integration)
**Type**: Prediction Evaluation / Information Theory / Belief Revision / Category Theory
**Trit**: 0 (ERGODIC)
**GF(3)**: Conserved via Cat# bicomodule structure
**Dependencies**: sox (optional, for sonification)
**Sources**: 
- [Spivak "All Concepts are Cat#" (ACT 2023)](https://topos.site/p/2023-act-tutorial)
- [Baker 2023 - AGM for Human Reasoning](https://ijcai.org/proceedings/2023/811)
- [Mazzola "The Topos of Music" (2002)](https://www.springer.com/gp/book/9783764357313)
- [arxiv:2505.13763 - LLM Metacognition](https://arxiv.org/abs/2505.13763)

Platform guidance

Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.