This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making. The defined quantities are decision-theoretic, but have information-theoretic analogues, e.g., they share some simple but key mathematical properties with Shannon entropy and mutual information, and can reduce to these quantities in particular settings. One interesting observation is that, the value of perception without prediction can be negative, while the value of perception together with prediction and the value of prediction alone are always nonnegative. The defined quantities suggest answers to practical questions arising in the design of autonomous decision-making systems. Example questions include: Do we need to observe and predict the behavior of a particular agent? How important is it? What is the best order to observe and predict the agents? The defined quantities may also provide insights to cognitive science and neural science, toward the understanding of how natural decision makers make use of information gained from different sources and operations.

翻译：本文旨在严格定义感知、预测、沟通与常识在决策中的价值。所定义的量具有决策论属性，但亦存在信息论对应物，例如它们与香农熵及互信息共享某些简单但关键的数学性质，并在特定情境下可简化为这些量。一个有趣的发现是：缺乏预测的感知价值可能为负，而预测配合感知的价值以及单独预测的价值则始终非负。这些定义量可为自主决策系统设计中的实际问题提供解答。典型问题包括：是否需要对特定智能体的行为进行观测与预测？其重要性如何？观测与预测各智能体的最优顺序为何？这些定义量亦可为认知科学与神经科学提供洞见，有助于理解自然决策者如何利用源自不同来源与操作的信息。