This paper is a continuation of my previous work on informational confidence. The main idea of this technique is to normalize confidence values from different sources in such a way that they match their informational content determined by their performance in an application domain. This reduces classifier combination to a simple integration of information. The proposed method has shown good results in handwriting recognition and other applications involving classifier combination. In the present paper, I will focus more on the theoretical properties of my approach. I will show that informational confidence has the potential to serve as a theory for learning in general by showing that this approach naturally leads us to the famous Yin/Yang symbol of Chinese philosophy, a classic symbol describing two opposing forces. Furthermore, a closer inspection of the opposing forces and their interplay will reveal a new information-theoretical meaning of the golden ratio, which describes the points where both confidence and counter-confidence merge into one force, with performance matching expectation. Although this is mainly a theoretical paper, I will present some practical results for handwritten Japanese character recognition.