Personal Decision Making (MCDM)
A Weighted Sum Model - the same multi-criteria decision-making technique used to prioritize supplier or vendor choices - pointed at your next job offer, apartment, or grad school pick.
Big personal decisions rarely fail from lack of options - they fail from comparing options on gut feel instead of the criteria that actually matter to you, weighted honestly.
01 /
The Problem.
// "I keep going back and forth"
When a decision has several options and several things that matter - salary vs. growth vs. location, or rent vs. commute vs. space - most people compare them in their head, where one loud criterion (usually the most recent conversation) quietly dominates the rest.
MCDM (Multi-Criteria Decision Making) techniques exist precisely to make every criterion's influence explicit and auditable - including to yourself.
02 /
Methodology.
// weighted sum model (simple additive weighting)
Criteria Weights
Each criterion gets a weight reflecting how much it matters to you; weights are normalized to sum to 100% regardless of the raw numbers entered.
Option Ratings
Each option is rated 1–10 against every criterion - your honest, criterion-by-criterion judgment, not an overall gut score.
Weighted Sum
Score = Σ(weight_i × rating_i). Every criterion contributes exactly its
declared weight - no criterion can quietly dominate.
Ranking
Options are ranked by total weighted score - and because the weights are visible, you can see exactly why one option won.
03 /
What’s in the build.
// shipped features
Any decision, any criteria
Type your own criteria, weights, and options - job offers, apartments, universities, even which laptop to buy.
Auto-generated rating matrix
Enter criteria and options once; the tool builds the full ratings grid for you to fill in.
Weight auto-normalization
Weights don't need to sum to exactly 100 - the tool normalizes them so the ranking is always valid.
Visual ranked bars
Final scores render as ranked horizontal bars, not just a table, so the winner is obvious at a glance.
Sample decision
A "choosing a job offer" example loads with criteria, weights, and ratings pre-filled.
Decision history
Past decisions and their winners are logged locally, so you can compare how you weighted similar choices before.
04 /
Stack & Outcome.
// what it took, what it shipped
Stack
Outcome
The decision gets made on the criteria you actually declared matter, weighted the way you actually weighted them - and if the "obvious" choice doesn't win, that's worth noticing too.
Live Tool