论文标题
关联信息和证明
Relating Information and Proof
论文作者
论文摘要
在数学中，信息是一个基于概率分布（通常是晦涩的起源）来测量不确定性（熵）的数字。在现实生活中，语言信息是一个基准，更精确地是一种公式。但是这样的公式应通过证据证明是合理的。我试图正式化这种信息的看法。证明信息的衡量标准是基于与正在考虑的公式有关的一组证明。这组可能的证据（“知识库”）定义了一种概率度量，并使用此度量定义了熵重量。该论文主要是概念性的，尚不清楚如何应用这种方法。
In mathematics information is a number that measures uncertainty (entropy) based on a probabilistic distribution, often of an obscure origin. In real life language information is a datum, a statement, more precisely, a formula. But such a formula should be justified by a proof. I try to formalize this perception of information. The measure of informativeness of a proof is based on the set of proofs related to the formulas under consideration. This set of possible proofs (`a knowledge base') defines a probabilistic measure, and entropic weight is defined using this measure. The paper is mainly conceptual, it is not clear where and how this approach can be applied.
