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Complete

Abstract

This paper proposes a voter-centric decision support framework for candidate matching based on reference point-based multi-criteria decision analysis (MCDA). Unlike traditional distance-based MCDA methods that derive reference points from candidate sets, the proposed approach models an individualized ideal candidate defined by voter survey responses. Candidates are evaluated by their distance to this voter-defined target profile, enabling personalized ranking and transparent comparisons. The framework integrates structuring, aggregation, and visualization mechanisms to support interpretability and informed decision-making. An empirical study based on data from the Polish Latarnik Wyborczy questionnaire demonstrates the feasibility of the approach and its strong alignment with existing voting advice tools, while offering improved flexibility through preference weighting and robustness analysis.

Paper Number

1430

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Aug 15th, 12:00 AM

Towards Voter-Centric Decision Support Framework for Candidate Matching Using Reference Point-Based MCDA

This paper proposes a voter-centric decision support framework for candidate matching based on reference point-based multi-criteria decision analysis (MCDA). Unlike traditional distance-based MCDA methods that derive reference points from candidate sets, the proposed approach models an individualized ideal candidate defined by voter survey responses. Candidates are evaluated by their distance to this voter-defined target profile, enabling personalized ranking and transparent comparisons. The framework integrates structuring, aggregation, and visualization mechanisms to support interpretability and informed decision-making. An empirical study based on data from the Polish Latarnik Wyborczy questionnaire demonstrates the feasibility of the approach and its strong alignment with existing voting advice tools, while offering improved flexibility through preference weighting and robustness analysis.