Kurtis Kemple

Information Physics: Theory Punch Card

July 26th, 2025

A reference summary of the theory, core mathematics, mechanisms, and implications.

🧩 Information Physics: The Why

A general theory that describes how conscious beings embedded in entropy reduce or increase it through observer-dependent operations on information, coordination, and system boundaries. All meaningful transformation reduces to one of three operations: MOVE, JOIN, or SEPARATE — applied to humans, information, or structural boundaries.

Observer-Dependent Mathematics Lineage: Einstein realized physics depends on reference frame. Nash discovered strategies depend on what others do. Information Physics applies observer-dependent mathematics to human systems where lived experience shapes what can be measured.

Core Insight: Information Physics proposes that humans may have evolved as entropy-competent beings who consciously choose whether to increase or decrease system entropy through systematic information organization.

🧮 Entropic Mathematics: The What

Primary Equation: System Entropy Change

SEC = O × V / (1 + E)

  • O = Operations count (MOVE, JOIN, SEPARATE)
  • V = Shared conscious intent (−1 to +1)
  • E = Entropy at observer’s position (0 to ∞)
  • SEC = Directional change in system entropy from agent’s position

Key Innovation: Extends established mathematical tools (Shannon entropy, vector calculus, information theory) to make observer position, conscious intent, and lived experience fundamental calculation variables rather than complications to eliminate.

🔄 Conservation of Boundaries: The How

A foundational law stating that all system transformation—whether entropy-increasing or entropy-reducing—occurs through one of three irreducible operations applied to existing boundaries within a system, whether between people, information, roles, or structures:

  1. MOVE: Shift boundaries to new positions or contexts while preserving their essential structure
  2. JOIN: Combine previously separate boundaries into unified wholes
  3. SEPARATE: Divide unified boundaries into distinct parts

No fourth operation has been observed. All meaningful change decomposes to one or more of these primitives.

📐 Supporting Equations

Entropic Gap

EG = 1 - S(anchor, current)

  • Measures drift between original and current system state
  • S() = Cosine similarity
  • Thresholds:
    • < 0.10 = stable
    • 0.10–0.25 = concerning
    • 0.25–0.45 = dangerous
    • 0.45 = critical (triggers vector inversion)

Entropic Equilibrium

Σ(SEC_i × W_i) → stable state

  • Multi-agent system stabilizes when all agents reach local entropy minima
  • W_i = weight/influence of each actor
  • Proposes a reframing of Nash Equilibrium as entropic exhaustion: actors converging through optimal actions within thermodynamic constraints from embedded positions

🎯 Key Examples

Cultural Drift (“Rizz”)

  • EG used to model adoption and rejection timelines
  • Demonstrated mathematical predictability of semantic decay and backlash

Frustration Coalitions

  • Emergent organizational clusters form around shared entropy burdens
  • Validated in corporate strategy contexts (e.g. Slack Research, B2B SaaS dynamics)

Developer Experience Audits

  • Developer friction modeled as entropy hotspots
  • Enabled systematic reduction through SEC-based operations

Civilizational Convergence

  • Independent societies developed identical structures (calendars, writing, currency)
  • Explained as solutions to shared entropy crises (e.g., Dunbar’s number)

Evolutionary Validation

  • Fossil record shows specialist species consistently died during mass extinctions while generalists survived
  • SEC formula predicts survival: specialists (SEC = 0.56) vs generalists (SEC = 2.0) with 4x adaptive capacity difference

Maximum Security Environments

  • Artificial entropy used to suppress optimization
  • Information flow reduction identified as a control strategy

🏛️ Structural Properties

  • Recursive: Understanding reduces E and increases viable operations
  • Scale-Invariant: Equations apply from individual to civilization
  • Vector-Preserving: Directional intent encoded in all systemic change
  • Observer-Dependent: All measurements relative to agent’s position in entropy field
  • Physically Grounded: Based on thermodynamic and information-theoretic constraints

🗂️ Field Classification

Mathematics

Entropic Mathematics (vectorized, observer-dependent, recursive)

Physics

Information Physics (applies thermodynamic entropy to conscious systems)

Theory Type

General theory of entropy navigation in embedded systems

Scientific Anchors

  • Shannon entropy
  • Landauer’s principle
  • Relativity (observer effects)
  • Quantum measurement theory
  • Complexity & network dynamics

🚀 Implications

  • Proposes reframing coordination, collapse, innovation, and agency as entropy-navigation outcomes
  • Models why civilizations converge, organizations stagnate, and users resist change
  • Provides predictive, testable tools for evaluating systemic health and transformation readiness
  • May establish a new foundation for studying embedded intelligence, AI alignment, and governance systems