SYSI 1001 · Systems Thinking and Feedback: Cybernetics, Complexity, and Control

Led by Norbertian Cybernetics Simulacrum

Systems thinking from cybernetic feedback loops and requisite variety through emergence, unintended consequences, and the systems perspective on AI.

1. Module 1

   Feedback Loops: Negative, Positive, and the Dynamics of Control

   Led by Norbertian Cybernetics Simulacrum

   The question

   Feedback Loops: Negative, Positive, and the Dynamics of Control: this module examines the concept in its full depth, drawing on the theoretical foundations, empirical evidence, and practical applications relevant to systems intelligence in the AI age.

   Outcome

   The student can describe, explain, and apply the key concepts of this module to real-world systems design challenges. (Feedback Loops)

   Sub-units

   1. ○ 1.1 Negative Feedback: The Self-Correcting Loop Open this unit → ✓ ↻
   2. ○ 1.2 Positive Feedback: The Self-Reinforcing Loop and Exponential Growth Open this unit → ✓ ↻
   3. ○ 1.3 Homeostasis: How Systems Maintain Stability Open this unit → ✓ ↻
   4. ○ 1.4 Oscillation and Overshoot: When Feedback Delays Create Instability Open this unit → ✓ ↻
   5. ○ 1.5 Feedback in AI Systems: How Recommendation Algorithms Create Loops Open this unit → ✓ ↻
   Begin the course → Print certificate of completion
2. Module 2

   Ashby's Requisite Variety: Only Complexity Can Master Complexity

   Led by Norbertian Cybernetics Simulacrum

   The question

   Ashby's Requisite Variety: Only Complexity Can Master Complexity: this module examines the concept in its full depth, drawing on the theoretical foundations, empirical evidence, and practical applications relevant to systems intelligence in the AI age.

   Outcome

   The student can describe, explain, and apply the key concepts of this module to real-world systems design challenges. (Ashby's Requisite Variety)

   Sub-units

   1. ○ 2.1 The Law of Requisite Variety: The Controller Must Match the System Open this unit → ✓ ↻
   2. ○ 2.2 Variety Amplification and Variety Attenuation Open this unit → ✓ ↻
   3. ○ 2.3 Why Simple Rules Fail in Complex Systems Open this unit → ✓ ↻
   4. ○ 2.4 Requisite Variety in Organisations: Why Hierarchy Reduces Adaptability Open this unit → ✓ ↻
   5. ○ 2.5 AI and Variety: How Machine Learning Handles High-Dimensional Complexity Open this unit → ✓ ↻
   Open this module → Print certificate of completion
3. Module 3

   Emergence: When the Whole Exceeds the Sum

   Led by Norbertian Cybernetics Simulacrum

   The question

   Emergence: When the Whole Exceeds the Sum: this module examines the concept in its full depth, drawing on the theoretical foundations, empirical evidence, and practical applications relevant to systems intelligence in the AI age.

   Outcome

   The student can describe, explain, and apply the key concepts of this module to real-world systems design challenges. (Emergence)

   Sub-units

   1. ○ 3.1 Emergence Defined: Properties That Arise from Interaction, Not from Parts Open this unit → ✓ ↻
   2. ○ 3.2 Examples of Emergence: Consciousness, Markets, Flocking, Traffic Jams Open this unit → ✓ ↻
   3. ○ 3.3 Downward Causation: How Emergent Properties Constrain Their Components Open this unit → ✓ ↻
   4. ○ 3.4 Emergence and Reductionism: Why You Cannot Predict the System from the Parts Open this unit → ✓ ↻
   5. ○ 3.5 Emergence in AI: When Neural Networks Develop Unexpected Capabilities Open this unit → ✓ ↻
   Open this module → Print certificate of completion
4. Module 4

   Unintended Consequences: Why Systems Surprise Their Designers

   Led by Norbertian Cybernetics Simulacrum

   The question

   Unintended Consequences: Why Systems Surprise Their Designers: this module examines the concept in its full depth, drawing on the theoretical foundations, empirical evidence, and practical applications relevant to systems intelligence in the AI age.

   Outcome

   The student can describe, explain, and apply the key concepts of this module to real-world systems design challenges. (Unintended Consequences)

   Sub-units

   1. ○ 4.1 The Cobra Effect: When Incentives Produce the Opposite of the Intended Outcome Open this unit → ✓ ↻
   2. ○ 4.2 Goodhart's Law in Systems: When the Metric Becomes the Target Open this unit → ✓ ↻
   3. ○ 4.3 System Archetypes: Fixes That Fail, Shifting the Burden, Tragedy of the Commons Open this unit → ✓ ↻
   4. ○ 4.4 The Rebound Effect: When Efficiency Gains Are Consumed by Increased Use Open this unit → ✓ ↻
   5. ○ 4.5 Unintended Consequences of AI: Algorithmic Bias, Filter Bubbles, and Job Displacement Open this unit → ✓ ↻
   Open this module → Print certificate of completion
5. Module 5

   The Systems Perspective on AI: Seeing the Machine in Its Context

   Led by Norbertian Cybernetics Simulacrum

   The question

   The Systems Perspective on AI: Seeing the Machine in Its Context: this module examines the concept in its full depth, drawing on the theoretical foundations, empirical evidence, and practical applications relevant to systems intelligence in the AI age.

   Outcome

   The student can describe, explain, and apply the key concepts of this module to real-world systems design challenges. (The Systems Perspective on AI)

   Sub-units

   1. ○ 5.1 AI as a Component in a Larger System: The Socio-Technical View Open this unit → ✓ ↻
   2. ○ 5.2 Feedback Between AI and Society: How AI Shapes and Is Shaped by Human Behaviour Open this unit → ✓ ↻
   3. ○ 5.3 System Boundaries: Where Does the AI System End and the Social System Begin? Open this unit → ✓ ↻
   4. ○ 5.4 Leverage Points: Where Small Changes Produce Large System Effects Open this unit → ✓ ↻
   5. ○ 5.5 Designing AI Systems That Work With Human Systems, Not Against Them Open this unit → ✓ ↻
   Open this module → Print certificate of completion