Agents care not only about the outcomes of collective decisions but also about how decisions are made. In many cases, both the outcome and the procedure affect whether agents see a decision as legitimate, justifiable, or acceptable. We propose a novel model for collective decisions that takes into account both the preferences of the agents and their higher order concerns about the process of preference aggregation. To this end we (1) propose natural, plausible preference structures and establish key properties thereof, (2) develop mechanisms for aggregating these preferences to maximize the acceptability of decisions, and (3) characterize the performance of our acceptance-maximizing mechanisms. We apply our general approach to the specific setting of dichotomous choice, and compare the worst-case rates of acceptance achievable among populations of agents of different types. We also show in the special case of rule selection, i.e., amendment procedures, the method proposed by Abramowitz, Shapiro, and Talmon (2021) achieves universal acceptance with certain agent types.

翻译：智能体不仅关注集体决策的结果，还关注决策的制定过程。在许多情况下，结果和程序都会影响智能体是否将决策视为合法、合理或可接受。我们提出了一种新颖的集体决策模型，该模型同时考虑了智能体的偏好及其对偏好聚合过程的高阶关切。为此，我们（1）提出了自然且合理的偏好结构并建立其关键属性，（2）开发了聚合这些偏好以最大化决策可接受性的机制，以及（3）刻画了我们的可接受性最大化机制的绩效表现。我们将这一通用方法应用于二元选择的特定场景，并比较了不同类型智能体群体中可实现的最坏情况可接受率。我们还证明，在规则选择（即修正程序）的特殊情况下，Abramowitz、Shapiro和Talmon（2021）提出的方法能在特定智能体类型中实现普遍可接受性。