Despite the large literature about non-additive value aggregation techniques, in the large majority of applied decision support processes, additive value aggregation functions are used. The main reasons for this may be the simplicity of the approach, minimum elicitation requirements, software availability, and the appeal of the underlying preference independence concepts that may be strengthened by an adequate choice of sub-objectives and attributes. However, in an applied decision support process, the decision maker(s) or the stakeholders decide on the sub-objectives and attributes to characterize the state of a system and they have to provide information that allows the decision analyst to express their preferences as a value function of these attributes. It is the task of the decision analyst to find the parameterization and parameter values of a value function that fits best the expressed preferences. We describe a value function elicitation process for the ideal morphological state of a lake shore, performed with stakeholders from federal and cantonal authorities and from environmental consulting companies in Switzerland. This process led to the elicitation of strongly non-additive and partly even non-concave value aggregation functions. The objective of this paper is to raise the awareness about the importance of carefully testing the assumptions underlying parameterized (often additive) value aggregation techniques during the preferences elicitation process and to be flexible regarding evaluating value functions that deviate from the often used additive aggregation scheme. This can lead to a higher confidence that additive aggregation is suitable for the specific decision problem or to the selection of alternative aggregation techniques that better represent the decision maker’s preferences in case additivity is violated.

尽管有大量关于非加性价值聚合技术的文献，但是在大多数应用的决策支持过程中，还是使用了加性价值聚合功能。造成这种情况的主要原因可能是方法的简单性，启发最低要求，软件的可用性，并可以通过子目标和属性的充分的选择余地，加强基础偏好独立概念的吸引力。但是，在应用决策支持过程中，决策者或利益相关者决定子目标和属性以表征系统状态，并且他们必须提供允许决策分析师将其偏好表达为信息的信息。这些属性的值函数。决策分析师的任务是找到最适合表示的偏好的值函数的参数化和参数值。我们描述了一个理想的湖岸形态状态的价值函数启发过程，该过程由联邦和州政府以及瑞士的环境咨询公司的利益相关者执行。这个过程导致了强烈的非加性和部分甚至非凹值聚合功能的产生。本文的目的是提高人们对在偏好激发过程中仔细测试参数化（通常为加性）值聚合技术的假设的重要性的认识，并灵活地评估偏离常用添加剂聚合方案的价值函数。