multiattribute

Multiattribute refers to something characterized by or involving multiple attributes, criteria, or features. In decision making and data analysis, multiattribute problems require evaluating options across several dimensions rather than a single measure. Attributes can be quantitative, qualitative, or a mix, and may require normalization or scaling so they can be meaningfully compared and aggregated.

In decision analysis, multiattribute decision making involves selecting, ranking, or prioritizing alternatives based on their performance across multiple criteria. To support consistent choices, researchers and practitioners often use frameworks that model preferences and combine attribute performance into an overall score or utility. These frameworks can handle compensatory trade-offs, where strong performance on some attributes can offset weakness on others, or non-compensatory approaches that impose thresholds or rules restricting trade-offs.

Common frameworks include multiattribute utility theory (MAUT) and multiattribute value theory (MAVT). MAUT models an overall utility function that reflects a decision maker’s preferences, often requiring careful elicitation of attribute utilities and, in some formulations, assumptions about attribute independence. MAVT emphasizes value or payoff functions for each attribute, which are weighted and aggregated to produce an overall value. Aggregation methods vary from additive models to more complex, non-linear or non-compensatory approaches. Related methods in the field include multiattribute conjoint analysis, analytic hierarchy process (AHP), and various multicriteria decision analysis (MCDA) techniques.

Applications of multiattribute approaches span engineering, economics, environmental planning, consumer research, and information systems. They provide structured, transparent processes for comparing alternatives and exploring how changes in attributes or weights affect decisions, while also highlighting areas of uncertainty and sensitivity.