Decision scoring
A score says this option is stronger on this criterion, given this evidence. If you cannot show the evidence, the score is decoration.
feature
Scores with reasons, not a mysterious average
Argumont scores options against criteria you can inspect. Weights are explicit. Reasons are stored. A model agreeing with itself is not a score.
How scoring works
You set criteria and weights before the run. Scores are whole numbers with explanations. User-confirmed facts can change those numbers.
Honesty limits
If you change weights after seeing the winner, you had a preference. The product cannot stop that. The trail can make it visible.
Related pages
Decision criteria
Criteria should be named before options are scored. Weights should reflect the actual trade, not the slide that needs to look balanced.
Decision confidence
Argumont reports confidence as a property of the analysis, not as enthusiasm. Missing evidence and contested claims should pull it down.
Compare options
Comparison work fails when each option is described in a different language. Argumont holds options to the same criteria, then lets you open every score.
Features
Each feature page describes something the product actually does, including limits. Team sharing beyond a private workspace is called out as future work.
Score a decision
Argumont challenges assumptions, researches evidence, compares competing approaches, and identifies the strongest decision.
Score a decision