How to improve Pawn promotion probability In my economic view, there are 3 types of economic companies. Pawn: Domestic companies which work in own countries Minor and Major pieces: Big companies which earn moneys in foreign countries King: Central centers in own countries Then, how to improve Pawn promotion probability? Supporting by Minor and Major pieces to Pawn may be good method. For example, if pawns in USA want to earn in China markets, supporting by Tesla, Microsoft and other Minor and Major pieces will be good. Conversely, if pawns in China want to earn in USA markets, supporting by Minor and Major pieces will be good. I recalled Mercari company’s fault. Mercari has tried to enter in USA markets only by itself, but it was failed. Because it had no support from minor and major pieces in Japan, such as TOYOTA or SoftBank.
2023/10/23. Highly Abstract Works
I thought about highly abstract works.
There may be 3 fields which will be able to monetize easily in this world.
I made this figure.
Natural Language, Machine Language, and Science are the 3 fields.
Natural Language is consisted from Bible(this can be replaced with in any cultures) and Law.
Science is consisted from Mathematics and Physics.
Machine Language may be consisted from Algorithms and Data structures.
In addition, I wonder that which type most appropriate for me is.
Maybe the field is where natural language and machine language are overlapped in the triangle.
(This triangle in the figure means that the inside is governed by 3 fields, and the outside is not.)
I like Patent Laws and Programming, so that the field is most appropriate for me, maybe.
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2023/10/23 - Exploring Abstract Concepts
I've been pondering deeply abstract concepts and identified three fields that seem to have significant monetization potential.
The illustration above depicts these three fields: Natural Language, Machine Language, and Science. Natural Language encompasses both the Bible (which can be substituted based on cultural references) and Law. Science is comprised of Mathematics and Physics, while Machine Language is potentially made up of Algorithms and Data structures.
Furthermore, I'm curious to determine which field aligns best with my interests. The overlap between natural language and machine language within the triangle might be the most fitting for me. The triangle in the illustration suggests that its interior is governed by the three fields, while the exterior isn't. Given my affinity for Patent Laws and Programming, this overlapping area may indeed be the most suitable for me.
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