It is really appropriate to use the word “Stalling” to describe the economy for now. Stalling is a phrase in airplane, states that if the altitude of an airplane is too low, the pilot have to pull up the aircraft joystick immediately to avoid an instant crash. However that does not save the airplane for five more seconds, the aircraft lost its lift with its upwarding angle. It must be accompanied with the other pilot boosting the engine power to save the emergency. That’s analogous to the economic condition right now. The eco-craft is stalling and if there is no more engine power, the whole thing would crash. So I think the key is to ensure that a bigger boost is provided to it. The question is that where should the growth come from. It is no more economic to start another construction plan.
When you wanna sell something, sell the anxiety related to it. Or, sell the wish with it. That’s how human works. For example, women products sell the anxiety with getting old and losing the previlage, and gym membership sells the hope that he will get more energetic and thinner.
Although many are PhDs, their ability varies widely. Here is my personal ranking. It is completely subjective and don’t take it seriously.
Working ability tier:
X: dissertation on theory based model with applied mathemaitics. Most MIT Phd and berkeley Phds get a degree with work like that.
S+: dissertation on theoretical econometrics and develops statistic estimators for others to use.
A: dissertation with good contributions utilizing empirical analysis, for example, aplied econometrics, applied statitics, machine learining and so on so forth.
B: dissertation with little incremental contributions utilizing empirical analysis, for example, aplied econometrics, applied statitics, machine learining and so on so forth.
C: PhDs who get a degree on management, business management, phychology, travel subjects who develop a new empirical method or a software package for others to use.
D: PhDs who get a degree on management, business management, phychology, travel subjects who did their research with empirical analysis.
E: PhDs who get a degree on environmental subjects.
Only tier S+ and X can take the name of a talent. Others literally know nothing and does not have the ability to capacitate himself any type of job that is offered.
Fei Zhang is a rude and inconsiderate man. Yu Guan is inflexible and thinks too high of himself. They are ordinary man if Bei Liu didn’t mate with them. So don’t complain if there are no talent in your team. You make them talent instead of the talent coming to you.
tough yet worth persisting
Many people are arrogant just to bluff, declare his status in front of you and thinks that can force you to respect him. To no avail of course. Many people boast only to hide self-abasement. Many people are stupid and blame others like coward rather than think about a solution.
The real world is full of ridicules. A smile and understanding is the ultimate wisdom of the universe. The path to everything is patience and waiting until things change. It will change at the end of the day. If you find someone disgusting, you can do the “work” way with him. (You talk trade exchange with a businessman, for example) If you find something not satisfying, think of a routine to optimize. If you find bad cooperation with someone, think what he will react under certain condition and optimize the way you work with him/her in advance. A bad beginning is not your fault. Yet complaint is. I have several videos that always encourage me when I feel really bad. Whereas bad examples to some extent, they show the power of perseverence.[1][2][3] They all share the same argument, when you feel exhausted it’s no longer worth it to persist, at that right moment things will become easier, you will be lucky dog and it’s almost close to a success. Hopefully my thoughts will encourage others temporarily stuck in the dark.
Why it is difficult to write the introduction for a bunch of scholars?
Many researchers have claimed that it is a pain in the ass to write the introduction section of a paper. However, it is not hard at all for other scholars. Why is that? What makes the difference?
The key lies on the starting point of a research. Those emprical scholars who find introduction section horrible start a research from the method and data and pretend to start from a question. After finishing all the other parts, they begin decorating the introduction part as if it was initialized from a question, which just does not exist in the first place. That’s where the pain comes from.
On the other hand, is it correct to start from a real (academic) question? It is, nonetheless hard. If you start from a question, you will find yourself searching for toolboxes that best fit your question and that process is painful either. Worthwhile one though. You will have all the toolboxes present in your mind and you can easily find the toolbox next time you find some questions interesting.
The critical point is how fast you can learn new methods. It requires a good background of mathematics. With a good one, the new methods come to you easily and your research would be much more flexible and elegant.
Random memory
Sometimes random memory pieces come to me without any warning when I was learning stuff. Maybe that’s the evidence that human beings have exploited much more capacity of his brain than we have expected, far more than 10%.
Avoid direct rays of the sun. It is really one of the three factors that are harmful to one’s eyes.
The key to thrive in an overall depression market
Today I read an article “When the balancesheet don’t want to strive anymore”[1]. Nonetheless the article is worth reading, a good point comes to me. It states a successful enterprise named Rakuten that thrives in an overall depression atmosphere. The key point is that only English is permissible as a working language when having a meeting.
Let me quote this:
而唯一拿得出手的互联网公司Rakuten（号称日本的亚马逊），其创始人三木谷浩史分享过两个秘诀：一是大量雇佣外籍人士，”Rakuten快速成长的原因就是我们聘用外国工程师”；二是公司2012年开始所有员在所有场合，都必须说英语。
Why have statistics destroyed the traditional academic community?
As an open community, the first and foremost thing is to ensure an easy replication. However, statistics driven topics, such as machine learning in CS majors and empirical studies in economics and management have ruined that base for private data. I think the academic community is dead after all the data and statistics driven academics coming along and thriving. There is no way to verify all of the results and the community is a merely bunch of untestable meterials.
Most academic frauds come out of fake data. And that’s only the tip of the iceberg. We have to reevaluate the threat that’s coming along to the community. Admittedly, most results are biased to some extent. The issue exaggerated as the data and statitics dominated the toolbox. If some pathways to the final result have been polluted by fake results, it would be a waste of efforts.
There is no need to adore a college. I like MIT so much not because of their hacking culture, but a high level engineering institute. A man grabs the megatrend and succeeds and so does a college. Stanford is merely one of the colleges that makes the most of the Bay Area miracle. I really adore the way Princeton does college the other way round. No chasing trends, no enrollment expansion,(and an awsome math community of course lol) just do what a college should do and keep it stupid simple.