DYNAMICSCORE

Cellular Automata & Game of Life

Tiny local rules aggregate into gliders, blinkers and chaos. Emergence needs no boss.

local moves → global blocks · recreated schematic

How it works

  • Each cell follows a tiny neighborhood rule, all at once.
  • Gliders, blinkers, chaos: the rulebook is pages, the zoo is endless.
  • Some rules compute — prediction needs simulation, not algebra.
Use → Prototype emergence; don't over-interpret any single run.
Watch out → Assuming simple rules mean simple outcomes.
Try it → Run Conway's Life from an R-pentomino; count generations to settle.
REDCAPE jobs → Explore · Explain
Free sources → Coursera M2 (Game of Life) • projectmesa/mesa-examples (MIT)
◍ Mentor soonSee REDCAPE playbook →

Attach to a lattice — combines with

Different families, shared jobs. Disagreement between them is where insight lives.

LEARNING
Replicator Dynamics (Fisher)
Winning strategies reproduce faster; adaptation speed rises with variation.
SHARED JOBS: EXPLAIN · EXPLORE
DIFFUSION
SIR Contagion
Susceptible–Infected–Recovered with an R0 tipping point. Same math for flu and pop-star fever.
SHARED JOBS: EXPLAIN · EXPLORE
GROWTH
Capital Share (r > g)
When returns on capital outrun growth, wealth concentrates without any villain.
SHARED JOB: EXPLAIN

More in Dynamics

Random Walk & Markets
Unpredictable steps accumulate; efficient markets leave mostly randomness behind.
Markov Chain
Next state depends only on now. Run it forward to get the long-run mix.
Polya Urn / Path Dependence
Early draws rewrite the odds — history locks in QWERTYs, VHSs and dominant cities.
← Polya Urn / Path DependenceSystem Dynamics / Feedback Loops →