Unweighted ranking for value-based decision making with uncertainty

TL;DR

This paper introduces FUW-VBDM, a human-centred decision-making framework that removes stakeholder bias by using fuzzy reasoning, and presents Rankzzy, an unweighted ranking method with proven consistency and improved computational efficiency.

Contribution

It proposes a novel fuzzy-based unweighted ranking method, Rankzzy, integrated into the FUW-VBDM framework to enhance human-centred decision making under uncertainty.

Findings

Rankzzy reduces computational costs in large-scale problems.

Rankzzy demonstrates strong ranking performance compared to existing methods.

The FUW-VBDM framework effectively incorporates qualitative criteria into decision making.

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.