Soft Computing and Learning Techniques in the Modeling of Humanistic Systems

Although modern computer is the most revolutionary and most powerful tool developed in the twenty’s century, it is almost useless for the application of this tool to the not well defined humanistic systems such as politics, law, or even the many hour-to-hour small decisions people make routinely and daily. This is in spite of the fact that the human action of the cognitive band, which is of the order of seconds, is much slower than the speed of the modern computer. In this paper, the author shall first examine the basic differences between the scientific systems and the humanistic systems. Then based on these resulting differences, the author shall propose a neural-soft-computing combined approach, which is naturally suited for the vague and difficult to define humanistic systems. These combined systems are developed during the last approximately twenty years. Yet, it has not applied to the humanistic systems in a systematic and extensive manner. Some of the neural-soft-computing systems, also known as neural-evolutionary operational systems are the combined use of neural network, or support vector machine, with fuzzy system or fuzzy logic and evolutionary operational techniques such as genetic algorithm. Several of these systems are summarized and discussed as to why these systems seem more promising for the handling of humanistic systems. To illustrate the effectiveness of the proposed approaches, several initial applications in the literature are summarized. Soft Computing and Learning Techniques in the Modeling of Humanistic Systems