Quantification of Training in Human Thought using Chaos Theory: Degree of Decreasing Entropy as an Indicator

Abstract

Many analytical methods used to quantify the technical training of human resources have been reported, but they are poor at assessing the training of human thought. Chaos theory and the degree of decreasing entropy have not yet been used in such analytics, although all phenomena—except for those in a completely fixed state (e.g., mathematics, historical facts, and true natural laws) or a random state (e.g., dialing a random digit)—are based on chaos theory. Chaos theory must therefore be considered in the quantification of the technical and intellectual training of human resources because human thinking is involved. This report compares a method for chaos theoretical quantification of human resource training to methods that do not involve chaos theory. The development of the ability to change to a fixed state from a chaotic state beyond the Feigenbaum point is the goal for the training in thinking; this process, which involves a decrease in entropy, is the most difficult, and important process in such training. In a chaotic state with an infinite number of solutions, humans can never show their own state using any method. In many reports, each goal in a fixed state is considered after thinking has been rearranged; however, a chaotic state in human thinking can never be expressed using indices such as productivity, efficiency, or job satisfaction. Indeed, many reported results are merely a part of human resource training, but the change of entropy in the fixed state is small (this corresponds to the “creativity” of artificial intelligence). By contrast, the change in entropy from a chaotic state to a fixed state is large, and this corresponds to human intuitive. Considered in terms of chaos theory, goal achievement for thinking must therefore be quantified in terms of the degree of decreasing entropy (i.e., the problem-solving speed, and degree of problem difficulty). This type of problem-solving speed is not about achieving ta goal but about creating a new idea. The concrete methods considered are the Schedule for the Evaluation of Individual Quality of Life-Direct Weighting method, the Kawakida Jiro method, and the Mandala chart. Based on the findings presented here, considering entropy change in the chaotic state should become an index for evaluating the creation of a new idea instead of the repetition of an existing idea, as this type of thinking is beyond the scope of artificial intelligence. Given that almost all natural phenomena, including human thinking, are based on chaos theory, using this theory can promote scientific development in all academic fields.