Decentralized Value Systems Agreements

TL;DR

This paper introduces a decentralized method for aggregating diverse value systems to find multiple value agreements, accommodating individual differences and improving utility in societal decision-making.

Contribution

It presents a novel decentralized optimization approach for generating multiple value agreements, unlike existing methods that seek a single consensus.

Findings

Applied to real-world data from participatory evaluations and surveys.

Achieved substantial utility improvements over existing aggregation techniques.

Demonstrated the method's ability to produce diverse value agreements.

Abstract

One of the biggest challenges of value-based decision-making is dealing with the subjective nature of values. The relative importance of a value for a particular decision varies between individuals, and people may also have different interpretations of what aligning with a value means in a given situation. While members of a society are likely to share a set of principles or values, their value systems--that is, how they interpret these values and the relative importance they give to them--have been found to differ significantly. This work proposes a novel method for aggregating value systems, generating distinct value agreements that accommodate the inherent differences within these systems. Unlike existing work, which focuses on finding a single value agreement, the proposed approach may be more suitable for a realistic and heterogeneous society. In our solution, the agents indicate their value systems and the extent to which they are willing to concede. Then, a set of agreements is found, taking a decentralized optimization approach. Our work has been applied to identify value agreements in two real-world scenarios using data from a Participatory Value Evaluation process and a European Value Survey. These case studies illustrate the different aggregations that can be obtained with our method and compare them with those obtained using existing value system aggregation techniques. In both cases, the results showed a substantial improvement in individual utilities compared to existing alternatives.