DYNAMICSCORE◉ LIVE LAB
Random Walk & Markets
Unpredictable steps accumulate; efficient markets leave mostly randomness behind.
How it works
- Each step is noise; position is the sum of all noise so far.
- Spread grows with √time — wider, not wiser.
- Skill shows only as a faint drift inside the wobble.
Use → Separate skill from luck before forecasting.
Watch out → Forecasting the wobble instead of the drift.
Try it → Fan out 30 walks in the lab, then add a small drift and see how hard it is to spot.
REDCAPE jobs → Reason · Predict
Free sources → SFI Random Walks tutorial • Coursera M9 (Randomness & Random Walks)
INTERACTIVE LABoriginal recreation — poke it
AVERAGE ENDPOINT
-2.4
A drift of 0.05 is real skill — yet single paths still wander both ways. Judge skill across many paths, not one.
Attach to a lattice — combines with
Different families, shared jobs. Disagreement between them is where insight lives.
LINEAR & NONLINEAR
Categorical & Threshold Models
Buckets and cutoffs turn messy attributes into testable if-then rules.
SHARED JOBS: REASON · PREDICT
DISTRIBUTIONSNormal Distribution
Bell-curve averaging: many small independent effects sum to a predictable center with thin tails.
SHARED JOBS: REASON · PREDICT
SORTING & PEER EFFECTSSchelling Segregation
Mild individual preference for similar neighbors aggregates to stark city-wide segregation.
SHARED JOBS: REASON · PREDICT
More in Dynamics
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.
Cellular Automata & Game of Life
Tiny local rules aggregate into gliders, blinkers and chaos. Emergence needs no boss.