Traceability
The decision stays linked to values, criteria, levels, and table version.
Traceable ordinal prioritization
The Prioriza Method
A framework for deciding what to address first when criteria are heterogeneous, preferences must be explicit, and the result needs explanation.
The problem
Many decisions mix signals that do not fit a single scale.
Urgency, impact, distance, risk, cost, fairness, opportunity, and effort cannot always be compared directly. Prioriza converts each signal into local priority levels and combines them with explicit aspect priorities.
The method does not replace specialized optimization techniques, AI, architecture, or management. It provides a clear layer for ordering, reviewing, and explaining decisions in those contexts.
Core idea
Values → levels → aspect priority → priority structure
Each aspect is leveled using its own criterion. Then the priority levels of the aspects express which criterion has greater decisional weight. The output preserves ties and leaves traces to review why an alternative ranked above, equal, or below.
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01
Values
Data or judgments recorded per aspect.
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02
Priority levels
Ordinal conversion within each aspect.
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03
Prioritized aspects
Explicit preference on which aspect matters more.
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04
Resulting structure
Order, ties, warnings, and review questions.
Method visualization
How data flows in Prioriza
Values
Distance
A: 2.4 km
B: 2.4 km
Local levels
Distance
A: level 1
B: level 1
Aspect levels
Distance: level 1
Prioriza: level 1
Priority structure
Tie
A and B: same level
Review another aspect
Each column lights up in sequence to show the method flow. Store A and Store B data flows from the original values through to the priority structure, preserving the tie at each stage.
Interactive demo
Prioriza live
Modify values or the APL of each aspect. The table recalculates the priority structure automatically.
Minimal example
Store A vs Store B: the tie also informs.
If two stores have the same distance, Prioriza does not force an artificial difference. It preserves the local tie and lets you ask which aspect is missing: availability, cost, hours, risk, or user preference.
Store
Distance
Local level
Store A
2.4 km
1
Store B
2.4 km
1
The result does not hide the indeterminacy: it turns it into a useful question for refining the decision.
Why it matters
Prioriza can produce answers and also better questions.
Explicit subjectivity
Preferences do not disappear: they are declared and can be discussed.
Explanation
The result can show dominant reasons, ties, and contradictions.
Refinement
A tie or surprise can point to what data or criterion is missing.
Open project
PDF, repository, and future tool.
The current manuscript is a theoretical draft in Spanish. The website also reserves a path for a future static Prioriza tool in the browser.