Decision Science (MCDA)

MAVT (Multi-Attribute Value Theory)

Evaluates alternatives under certainty using value functions (simplified MAUT without risk modeling)

Rubric Type

weighted-sum

Complexity

medium

Extractor

technical

Required Inputs

SolveRight's AI extractor automatically derives these data points from your decision description:

  • ✓attributes
  • ✓value functions
  • ✓attribute weights
  • ✓performance scores

Best For

ConsultantsProduct Managers

How MAVT (Multi-Attribute Value Theory) Works in SolveRight

When you run a decision through SolveRight, MAVT (Multi-Attribute Value Theory) is one of up to 155 frameworks that analyze your options simultaneously. The AI extractor identifies 4 key data points from your decision description, then the weighted-sum rubric computes a normalized 0-100 score for each option. This score is combined with results from other frameworks to produce your overall ranking, with contradiction detection highlighting where MAVT (Multi-Attribute Value Theory)disagrees with other methodologies.

MAVT (Multi-Attribute Value Theory) — Frequently Asked Questions

What is MAVT (Multi-Attribute Value Theory)?+
Evaluates alternatives under certainty using value functions (simplified MAUT without risk modeling). In SolveRight, MAVT (Multi-Attribute Value Theory) uses a weighted-sum rubric to compute a normalized 0–100 score for each option.
When should I use MAVT (Multi-Attribute Value Theory)?+
MAVT (Multi-Attribute Value Theory) is best suited for Decision Science (MCDA) decisions. It evaluates factors like attributes, value functions, attribute weights, making it valuable when you need systematic multi-criteria analysis backed by decision theory.
How does SolveRight use MAVT (Multi-Attribute Value Theory)?+
SolveRight runs MAVT (Multi-Attribute Value Theory) alongside up to 154 other frameworks simultaneously. The AI extractor identifies 4 key data points from your decision description, then the weighted-sum rubric computes deterministic scores. If MAVT (Multi-Attribute Value Theory) disagrees with other frameworks, contradiction detection highlights the divergence.
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Related Decision Science (MCDA) Frameworks

Weighted Decision Matrix

Scores options against weighted criteria for systematic comparison

Analytic Hierarchy Process (AHP)

Derives priority weights from pairwise comparisons with consistency check

Multi-Criteria Decision Analysis (MCDA)

Formal multi-criteria evaluation combining multiple scoring methods

Regret Minimization Framework

Evaluates options through the lens of future regret minimization

ANP (Analytic Network Process)

Extends AHP to handle interdependencies and feedback between criteria and alternatives

TOPSIS

Ranks alternatives by closeness to ideal solution and distance from anti-ideal solution