hard-choice-model
Skill3 starsChanged 6 months agoMITView on GitHub
Use the hard choice model to classify a decision by impact and ease of comparison. Use when deciding whether to move quickly, refine options, gain confidence, or apply a decision matrix.
In this repository23 other files it gives its agents.
- abstraction-ladderingSkill
- balancing-feedback-loopSkill
- concept-mapSkill
- confidence-determines-speed-vs-qualitySkill
- conflict-resolution-diagramSkill
- connection-circlesSkill
- cynefin-frameworkSkill
- decision-matrixSkill
- eisenhower-matrixSkill
- first-principlesSkill
- iceberg-modelSkill
- impact-effort-matrixSkill
- inversionSkill
- ishikawa-diagramSkill
- issue-treesSkill
- ladder-of-inferenceSkill
- minto-pyramidSkill
- ooda-loopSkill
- productive-thinking-modelSkill
- reinforcing-feedback-loopSkill
- second-order-thinkingSkill
- situation-behavior-impactSkill
- six-thinking-hatsSkill
The file
Files
Hard Choice Model
Overview
A decision-making tool to help understand the nature of a decision based on two key factors:
- Impact of the decision
- Ease of comparing options
How to Use It
Decision Types
- No-brainer
- Low impact
- Easy to compare options
- "Move fast and go with your gut feeling"
- Apples/Oranges Choice
- Low impact
- Difficult to compare options
- Refine options based on what's truly important
- Big Choice
- High impact
- Easy to compare options
- "Need to gain confidence before making the decision"
- Hard Choice
- High impact
- Difficult to compare options
- Recommended to use a decision matrix to evaluate options
Visual Representation
Image: Hard choice model grid showing four quadrants of decision types
Source
"Mental models for designers" by Wes O'Haire
Key Takeaway
Categorizing your decision helps you choose the most appropriate approach to making it.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.