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WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making.

Stephan Pajer, Marc Streit, Thomas Torsney-Weir

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    WeightLifter enhances Multi-Criteria Decision Making (MCDM) by visually exploring weight spaces. This interactive technique improves decision efficiency and clarifies uncertainty for complex choices with multiple criteria.

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    Area of Science:

    • Decision Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Multi-Criteria Decision Making (MCDM) commonly uses weighted summary scores for ranking alternatives.
    • Current methods often rely on vague intuition for setting weights, leading to abstractness for decision-makers.
    • Existing weight space exploration is typically point-wise, limiting comprehensive analysis.

    Purpose of the Study:

    • To introduce WeightLifter, a novel interactive visualization technique for weight-based MCDM.
    • To facilitate the exploration of weight spaces with up to ten criteria.
    • To improve understanding of decision sensitivity to weight changes and identify optimal weight regions.

    Main Methods:

    • Developed WeightLifter, an interactive visualization technique for exploring weight spaces in MCDM.
    • Conducted a requirement analysis for weight-based MCDM and designed an interactive workflow.
    • Evaluated the technique through a usage scenario in automotive engineering and user feedback.

    Main Results:

    • WeightLifter enables efficient localization of weight regions where specific solutions rank highly.
    • The technique helps filter out solutions that cannot achieve high ranks under plausible weight combinations.
    • Users reported increased efficiency in MCDM and greater awareness of decision uncertainty.

    Conclusions:

    • WeightLifter significantly enhances the efficiency and transparency of weight-based MCDM.
    • The interactive visualization approach improves decision-maker understanding of weight sensitivity and solution robustness.
    • Findings suggest broader applicability of WeightLifter across various decision-making domains.