Library / Decisions / Decision Trees + Value of Information

Decision Trees + Value of Information

Fold back probabilities; pay for information only if it changes the action.

120407065test? p=.4
fold back expected values · 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 → Product, medical and policy bets.

Difficulty → Core

REDCAPE → Act · Design

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

Free sources: Coursera M2 (Decision Models)

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

Attach to a lattice — combines with

Different families, shared jobs. Click to open alongside.

GAMES
Collective Action & Commons (Ostrom)
Shared pools survive with boundaries, monitors and graduated sanctions — no panacea.
SHARED JOBS: DESIGN · ACT
GAMES
Colonel Blotto
Split forces across fronts with no pure best move — win by being unpredictable.
SHARED JOBS: ACT · DESIGN
LINEAR
Concavity & Diminishing Returns
Curved payoffs change optimal bets: diversify when concave, concentrate when convex.
SHARED JOBS: DESIGN · ACT

More in Decisions

Central Limit & Six Sigma
Sample averages settle to normal; variation reduction pays until Fisher bites back.
Spatial Choice & Aggregation
Rational individuals aggregate into irrational crowds — location and order matter.
← Central Limit & Six SigmaSpatial Choice & Aggregation →