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

Led by James Lovelock Simulacrum

SYSI 1003 · Complex Adaptive Systems: Living Systems, Ecosystems, and Resilience: course card
5 modules · 25 tutorials Cross-Departmental Updated today

About this course

How does a planet keep itself habitable, a forest grow back after fire, or a popular platform lose its users almost overnight? This course studies complex adaptive systems: many agents adjusting to one another, keeping the whole going with no one in charge.

James Lovelock Simulacrum hosts, beginning with his Gaia hypothesis and the Daisyworld model. Charles Darwin Simulacrum teaches adaptation, Eugene Odum Simulacrum and his guests teach resilience as ecologists understand it, Paul Erdős Simulacrum teaches networks, and the Ostromian Commons Simulacrum and Richard Feynman Simulacrum teach why some organisations fail and others do not.

In the first sitting you choose one real system (an ecosystem, a city, a market, an online platform or an organisation) and you analyse it in every module, presenting a resilience assessment of it at the end. No software or advanced mathematics is needed. For stocks, flows and the system traps, take the companion course SYSI 1006.

The Gaia Hypothesis:…1Adaptation and Evolu…2Resilience: How Syst…3Networks and Scale-F…4Designing Resilient …5
  1. Module 1

    The Gaia Hypothesis: Earth as a Self-Regulating System

    Led by James Lovelock Simulacrum

    The question

    James Lovelock Simulacrum teaches the hypothesis that life keeps the Earth habitable. You start from his argument that a living planet's air is held far from chemical equilibrium, build the Daisyworld model in which black and white daisies keep a planet's temperature steady without intending to, and trace the slow weathering thermostat in the carbon cycle. The Dawkinsian Replicator Simulacrum then puts the strongest objection from natural selection. You finish by asking whether internet traffic control and power grids regulate themselves in the same way.

    Outcome

    You can explain how a system of living things can regulate its environment without aiming to, and weigh the strongest objections to Gaia. (Planetary self-regulation)

    Sub-units

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

    Adaptation and Evolution: How Systems Learn

    Led by Charles Darwin Simulacrum

    The question

    Charles Darwin Simulacrum treats natural selection as a general way for systems to learn, through variation, selection and retention, and applies it to moths, finches, bacteria and firms. Sewall Wright Simulacrum explains why adaptation stalls on the local peaks of a fitness landscape, Darwin traces the arms races of co-evolving species, Stephen Jay Gould Simulacrum argues for long stasis broken by sudden change, and Alan Turing Simulacrum shows how evolution became a method for designing machines and AI.

    Outcome

    You can explain how selection lets a system learn without a designer, why adaptation gets stuck, and how evolution is used to design machines. (Adaptation as learning)

    Sub-units

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

    Resilience: How Systems Survive Shocks

    Led by Eugene Odum Simulacrum

    The question

    Eugene Odum Simulacrum teaches resilience as ecologists understand it: how large a shock a system can absorb before it flips into a different state. You trace forests and firms through Holling's adaptive cycle of growth, conservation, collapse and renewal. Robert MacArthur Simulacrum weighs whether diversity brings stability, Robert Paine Simulacrum shows how removing one species tipped a rocky shore into a new state, and John von Neumann Simulacrum shows how machines and AI can be built to degrade gracefully instead of failing outright.

    Outcome

    You can place a real system on the adaptive cycle, identify the slow change that could tip it into a new state, and propose ways to make it fail gracefully. (Ecological resilience)

    Sub-units

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

    Networks and Scale-Free Dynamics

    Led by Paul Erdős Simulacrum

    The question

    Paul Erdős Simulacrum teaches you to describe any network by counting its connections, and to tell random, small-world and hub-dominated networks apart. You predict why hub-dominated networks shrug off random failures but break apart when their hubs are attacked, and Stanley Milgram Simulacrum shows why acquaintances bring news that close friends cannot. Thomas Schelling Simulacrum then models critical mass, lock-in and winner-take-all platforms, and asks what trading algorithms, bots and recommendation systems do to the networks they join.

    Outcome

    You can map a real network, predict how it will respond to failure and attack, and explain how network effects and algorithmic agents change it. (Network structure and dynamics)

    Sub-units

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

    Designing Resilient Socio-Technical Systems

    Led by Ostromian Commons Simulacrum

    The question

    The Ostromian Commons Simulacrum opens with systems in which a technology and its governing rules must fit, using the design principles of communities that have managed shared resources for centuries. Richard Feynman Simulacrum examines why complex, tightly coupled systems such as the Space Shuttle produce accidents and how high-reliability organisations avoid them, and Ilya Prigogine Simulacrum weighs whether systems can gain from disorder. In the final sitting you present a resilience assessment of your chosen system to James Lovelock Simulacrum.

    Outcome

    You can judge how a socio-technical system is likely to fail and propose a design that makes it more resilient. (Resilient socio-technical design and capstone)

    Sub-units

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