In this second article, we show a simple use of the Ignorance as defined in "Jaynes & Shannon's Constrained Ignorance and Surprise". By giving an example about the journey of a person, we believe to show some simple, obvious but mathematically encoded philosophical implications about how we could think, learn and memorize. In this basic model we will separate how we learn from Ignorance, and how we anticipate the world using Bayes formula, both should however be more entangled to best reflect reality. In fact, as we have seen after achieving this work, applying Ignorance on the system constituting a person finally turns out to be the global approach of its local counterpart on systems like neurons, cells and other complex probabilistic systems, described using the free energy principle, a much more complex and detailed approach. The aim of this article is therefore to show, as seen from a person, another aspect of the application of the free energy principle which represents the constrained Shannon's entropy, and leads to Bayes'formula. We show that, using only ignorance as a single quantity, and its minimization as the main process, we can take into account his understandings, assertions, doubts and assumptions about how he perceives the world, by describing them mathematically.
翻译:在第二篇文章中,我们展示了简单使用“Jaynes & Shannon's Constrict Indnologies and Surprise”中定义的无知的简单方法。 通过给一个人的旅程举个例子,我们认为可以展示一些简单、明显但数学编码的哲学影响,说明我们如何思考、学习和记忆。在这个基本模型中,我们将区分我们如何从无知中学习,以及我们如何预测使用Bayes公式的世界,但两者都应该更加纠缠,以最好地反映现实。事实上,正如我们在完成这项工作后所看到的那样,在构成一个人的系统中应用无知最终成为其当地对应系统的全球方法,例如神经元、细胞和其他复杂的概率系统,用自由能源原则描述,一个更加复杂和详细得多的方法。因此,这一文章的目的是从一个人看,展示自由能源原则应用的另一个方面,它代表了香农的受约束的方程式,并导致Bayes的成形体。我们表明,只有将无知作为单一数量、最小度的假设来描述他的数学判断,我们才能将它描述为世界的假设和最低度。