Library / Decisions / Spatial Choice & Aggregation

Spatial Choice & Aggregation

Rational individuals aggregate into irrational crowds — location and order matter.

outlier pulls line
best-fit line ≠ cause · recreated schematic
INTERACTIVE LAB

Tolerance sandbox (original recreation)

happy 76% • avg. similar neighbors 49% • red ring = wants to move

DECISIONS

When to use / misuse

Use → Voting, siting, assortment. Aggregate rules, not vibes.

Difficulty → Advanced

REDCAPE → Reason · Predict

agent: spatial-choice-checker
family: Decisions
combine_with: [two other families]
rule: state failure mode before acting

Free sources: Coursera M2 (Preference Aggregation)

← LibrarySee Playbook →
◍ Ask mentor about this →See REDCAPE playbook →

Attach to a lattice — combines with

Different families, shared jobs. Click to open alongside.

LINEAR
Categorical & Threshold Models
Buckets and cutoffs turn messy attributes into testable if-then rules.
SHARED JOBS: REASON · PREDICT
LINEAR
Linear Regression
Best-fit line plus a causation audit. A big coefficient is not a big cause.
SHARED JOBS: REASON · PREDICT
DISTRIBUTIONS
Normal Distribution
Bell-curve averaging: many small independent effects sum to a predictable center with thin tails.
SHARED JOBS: REASON · PREDICT

More in Decisions

Central Limit & Six Sigma
Sample averages settle to normal; variation reduction pays until Fisher bites back.
Decision Trees + Value of Information
Fold back probabilities; pay for information only if it changes the action.
← Decision Trees + Value of InformationNormal Distribution →