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.

翻译：基于价值的决策面临的最大挑战之一在于价值观的主观性。特定决策中价值观的相对重要性因人而异，且个体对"与价值观对齐"在具体情境中含义的理解也可能不同。尽管社会成员很可能共享一套原则或价值观，但研究发现其价值体系——即对这些价值观的解读方式及赋予它们的相对重要性——存在显著差异。本文提出一种聚合价值体系的新方法，通过生成差异化价值协议来容纳这些体系间的固有差异。与现有聚焦于寻求单一价值协议的研究不同，本方法可能更适用于现实中的异质性社会。在我们的解决方案中，智能体既表明其价值体系，也显示其愿意让步的程度。随后，采用去中心化优化方法找到一组协议。本研究已利用参与式价值评估过程数据和欧洲价值观调查数据，在两个现实场景中应用以识别价值协议。这些案例展示了本方法可获得的多种聚合结果，并将其与现有价值体系聚合技术所得结果进行比较。在两个案例中，结果显示本方法在个体效用方面相较现有替代方案均有显著提升。