SYSI 1003 · Complex Adaptive Systems: Living Systems, Ecosystems, and Resilience

Led by James Lovelock Simulacrum

Complex adaptive systems from the Gaia hypothesis and living systems through ecosystem dynamics, resilience theory, and the parallels between ecological and technological systems.

1. Module 1

   The Gaia Hypothesis: Earth as a Self-Regulating System

   Led by James Lovelock Simulacrum

   The question

   The Gaia Hypothesis: Earth as a Self-Regulating System: 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 Gaia Hypothesis)

   Sub-units

   1. ○ 1.1 Lovelock's Gaia: The Earth as a Living System Open this unit → ✓ ↻
   2. ○ 1.2 The Daisyworld Model: How Feedback Creates Planetary Homeostasis Open this unit → ✓ ↻
   3. ○ 1.3 The Carbon Cycle and Climate Regulation Open this unit → ✓ ↻
   4. ○ 1.4 Critiques of Gaia: Is the Earth Really Self-Regulating? Open this unit → ✓ ↻
   5. ○ 1.5 Gaia and AI: Can Technological Systems Be Self-Regulating? Open this unit → ✓ ↻
   Begin the course → Print certificate of completion
2. Module 2

   Adaptation and Evolution: How Systems Learn

   Led by James Lovelock Simulacrum

   The question

   Adaptation and Evolution: How Systems Learn: 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. (Adaptation and Evolution)

   Sub-units

   1. ○ 2.1 Natural Selection as a Learning Algorithm Open this unit → ✓ ↻
   2. ○ 2.2 Fitness Landscapes: Peaks, Valleys, and the Topology of Adaptation Open this unit → ✓ ↻
   3. ○ 2.3 Co-Evolution: When Organisms Adapt to Each Other Open this unit → ✓ ↻
   4. ○ 2.4 Punctuated Equilibrium: Long Periods of Stability Interrupted by Rapid Change Open this unit → ✓ ↻
   5. ○ 2.5 Evolutionary Computation: Using Natural Selection to Optimise AI Systems Open this unit → ✓ ↻
   Open this module → Print certificate of completion
3. Module 3

   Resilience: How Systems Survive Shocks

   Led by James Lovelock Simulacrum

   The question

   Resilience: How Systems Survive Shocks: 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. (Resilience)

   Sub-units

   1. ○ 3.1 Resilience Defined: The Capacity to Absorb Disturbance and Reorganise Open this unit → ✓ ↻
   2. ○ 3.2 The Adaptive Cycle: Growth, Conservation, Release, Reorganisation Open this unit → ✓ ↻
   3. ○ 3.3 Redundancy and Diversity: The Foundations of Resilient Systems Open this unit → ✓ ↻
   4. ○ 3.4 Tipping Points: When a System Shifts to a New State Open this unit → ✓ ↻
   5. ○ 3.5 Resilience in Technological Systems: Building AI That Fails Gracefully Open this unit → ✓ ↻
   Open this module → Print certificate of completion
4. Module 4

   Networks and Scale-Free Dynamics

   Led by James Lovelock Simulacrum

   The question

   Networks and Scale-Free Dynamics: 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. (Networks and Scale-Free Dynamics)

   Sub-units

   1. ○ 4.1 Network Topology: Random, Small-World, and Scale-Free Networks Open this unit → ✓ ↻
   2. ○ 4.2 Hubs and Vulnerability: Why Scale-Free Networks Are Robust to Random Failure but Fragile to Targeted Attack Open this unit → ✓ ↻
   3. ○ 4.3 The Strength of Weak Ties: How Distant Connections Spread Information Open this unit → ✓ ↻
   4. ○ 4.4 Network Effects in Technology: Winner-Take-All Dynamics Open this unit → ✓ ↻
   5. ○ 4.5 AI in Networks: How Algorithmic Agents Reshape Network Dynamics Open this unit → ✓ ↻
   Open this module → Print certificate of completion
5. Module 5

   Designing Resilient Socio-Technical Systems

   Led by James Lovelock Simulacrum

   The question

   Designing Resilient Socio-Technical Systems: 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. (Designing Resilient Socio-Technical Systems)

   Sub-units

   1. ○ 5.1 The Socio-Technical System: Technology Embedded in Social Context Open this unit → ✓ ↻
   2. ○ 5.2 Normal Accidents: Why Complex Tightly-Coupled Systems Fail Open this unit → ✓ ↻
   3. ○ 5.3 High-Reliability Organisations: How Some Systems Avoid Catastrophe Open this unit → ✓ ↻
   4. ○ 5.4 Antifragility: Systems That Gain from Disorder Open this unit → ✓ ↻
   5. ○ 5.5 Designing AI Systems for Resilience: Redundancy, Diversity, and Graceful Degradation Open this unit → ✓ ↻
   Open this module → Print certificate of completion