The economy may be as simple as three people model: a farmer, a fisher, and a wine brewer, where the economy can grow only when: 1. the fisher wants to eat breads 2. the farmer wants to eat fishes 3. the fisher and/or farmer want to drink wine 4. the brewer wants to eat more breads/fishes.
- As long as we have labors, tools, and demands, we have economy growth
- If we do not know what to do, we can focus on the demand side of the economy
We have so many capable people who are eager to learn new skills and work. Our companies have still enough liquidity. And, most importantly, our demand did not disappear following the slowing down of malls, restaurants, business area, or entertainments.
Therefore, our economy has still been growing while we may have to re-organize our industry segmentations to measure it since the consumption now have to go through some intangible technology, in stead of conventional routines, to reach product/service.
Our method to build datasets of those new industrial segments, which we need to train for our ML/DL economic forecasting algorithms, is to trace back the the only unchanged part of model - the demand.
The economy may be as simple as three people model: a farmer, a fisher, and a wine brewer, where the economy can grow only when: 1. the fisher wants to eat breads 2. the farmer wants to eat fishes 3. the fisher and/or farmer want to drink wine 4. the brewer wants to eat more breads/fishes.
- As long as we have labors, tools, and demands, we have economy growth
- If we do not know what to do, we can focus on the demand side of the economy
We have so many capable people who are eager to learn new skills and work. Our companies have still enough liquidity. And, most importantly, our demand did not disappear following the slowing down of malls, restaurants, business area, or entertainments.
Therefore, our economy has still been growing while we may have to re-organize our industry segmentations to measure it since the consumption now have to go through some intangible technology, in stead of conventional routines, to reach product/service.
Our method to build datasets of those new industrial segments, which we need to train for our ML/DL economic forecasting algorithms, is to trace back the the only unchanged part of model - the demand.