How much can be learned from a noisy signal about the state of the world not only depends on the accuracy of the signal, but also on the distribution of the prior. Therefore, we define a general information system as a tuple consisting of both a signal technology and a prior. In this paper we develop a learning order for general information systems and characterize the order in two different ways: first, in terms of the dispersion of posterior beliefs about state quantiles and, second, in terms of the value of learning for two different classes of decision makers. The first class includes all agents with quasi-linear quantile preferences, and the second class contains all agents with supermodular quantile preferences.

从关于世界状态的嘈杂信号中可以学到多少不仅取决于信号的准确性，还取决于先验的分布。因此，我们将通用信息系统定义为由信号技术和先验组成的元组。在本文中，我们为一般信息系统开发了一个学习顺序，并以两种不同的方式描述了该顺序：第一，根据关于状态分位数的后验信念的分散，第二，根据两个不同类别的学习价值决策者。第一类包括所有具有准线性分位数偏好的代理，第二类包含所有具有超模分位数偏好的代理。