Unweighted ranking for value-based decision making with uncertainty

2026-05-13

AI Summary

This study addresses the normative bias and insufficient value alignment in value-oriented decision-making by intelligent systems, which often arise from human-assigned weights. To overcome these limitations, the authors propose the FUW-VBDM framework, which eschews predefined weights and instead formulates decision-making as a feasible-solution search problem within a weight space using a scoring function defined over a fuzzy decision variable domain. The framework introduces Rankzzy, a novel weight-free ranking mechanism that leverages fuzzy set theory and Pythagorean mean aggregation to quantify uncertainty and ensure reasoning consistency without relying on subjective weighting. Experimental results demonstrate that the proposed approach significantly reduces computational overhead, achieves superior ranking performance across diverse value-oriented scenarios, and exhibits strong generalization capabilities.

Abstract

As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concerns. Their alignment with human values remains a critical challenge because it can jeopardise the integrity and security of citizens. For this reason, an innovative human-centred and values-driven approach to decision making is required. In this work, we introduce the Fuzzy-Unweighted Value-Based Decision Making (FUW-VBDM) framework, where agents incorporate both quantitative and qualitative criteria to generate human-centred decisions. We also address the normative bias introduced by stakeholders with arbitrary weights by removing prior weights and introducing a fuzzy domain of decision variables defined for a score function. This concept allows us to generalise any VBDM problem as the search for feasible solutions when optimising the score in the weight domain. To provide a solution to FUW-VBDM, we present Rankzzy, a customizable unweighted ranking method that integrates fuzzy-based reasoning to quantify uncertainty. We mathematically prove the consistency of the Rankzzy for any admissible configuration selected by stakeholders. We show the applicability of our method through an illustrative case study, which we also use as a running example. The evaluation conducted indicates a reduced computational cost in large-scale value-based decision-making problems and a strong rank performance regarding existing approaches when employing the aggregation via Pythagorean means.

Problem

• value-based decision making
• uncertainty
• unweighted ranking
• human-centred AI
• normative bias

Innovation

• unweighted ranking
• value-based decision making
• fuzzy reasoning
• uncertainty quantification
• Pythagorean means