Induction, Deduction and Its Application
Induction is the basic and starting step of discovering causality. Through induction, knowledge accumulates scalability. Induction in the early stages of research is a fundamental methodology.
And some knowledge is completed through induction, and its inferential results work almost perfectly.
Deduction is the observation of new possibilities with the aid of induction. Deduction requires a special premise because the middle part needs a kind of quantum jump. Deduction can take a leap from the existing framework to another stage.
So, most fresh or remarkable discoveries involve a process of deduction. Without the help of deduction, it could be difficult to get out of the existing obsolescence in the past.
An example of application,
1. If I predict the direction of stocks with specific technical indicators or simple casual correlations, I may find that the forecasts are often deviated. Of course, there are cases where it fits perfectly during a certain period of time.
2. However, since it is impossible to guarantee continuous predictability, and if I recognize the limitation of plotting casual relations with limited information, I eventually have to admit random stock price movements respectably.
3. And randomness implies the convergence of probabilistic statistics. Meanwhile, that does not mean a complete fixed result.
4. In this part, the importance of deduction emerges. And it is presumably recognized that it starts with the basic assumption of acquiring enough and sufficient induction experience.
5. As a result, the outcome of the game is determined by the deduction of the game.
6. It is hard for me to get out of the idea that the consequential judgement of this final stage of deduction will eventually be the competence of an individual.