What is the quantification of scientific thinking?

Hello, scientific thinking, also known as scientific logic, is a theoretical system that forms and applies to scientific cognition activities and the ways and means of processing perceptual cognition materials; It is the organic integration of various scientific thinking methods in the process of the unity of truth understanding, and it is the product of human practical activities.

In scientific understanding activities, scientific thinking must abide by three basic principles: it requires strict logic in logic and achieves the unity of induction and deduction; In terms of methodology, it requires dialectical analysis and synthesis of two methods of thinking; In terms of system, it is necessary to achieve the consistency between logic and history, and to achieve the unity of theory and practice in concrete history.

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The main features of scientific thinking.

Generally speaking, scientific thinking has the following main characteristics: objectivity, accuracy, testability, predictability and universality.

1.Objectivity: Scientific thinking should be based on reality, which can truthfully reflect the essence and laws of things, that is, try to truly reflect the object of understanding.

This is the basis and necessary starting point for scientific thinking, and if we start from books or from certain theories and principles, and do not respect facts and do not attach importance to the results of practice, it cannot be scientific thinking.

2.Accuracy: Scientific thinking is not only thinking that conforms to the essence and laws of things, it is also thinking that follows certain logical rules, and can use a large number of thinking means and cognitive tools in the thinking process, so it has strong accuracy.

With the development of understanding and the improvement of cognitive ability, and with the continuous improvement of various means of thinking and cognitive tools, the level of accuracy of scientific thinking will become higher and higher.

3.Temiability: The reason why scientific thinking is scientific is that the results of its thinking can be verified, and scientific thinking is thinking that can stand the test of practice.

Scientific thinking is not thinking that does not make mistakes, but refers to thinking that can withstand the test of practice, and can constantly adhere to the truth and correct errors in the test of practice. What was right in the past, and as practice evolves, mistakes may arise today; Today is the right thing, with the development of practice, there is no guarantee that tomorrow will still be correct, therefore, scientific thinking is the thinking that must be constantly tested by practice, and it is the thinking that constantly adheres to the truth and corrects mistakes.

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To put it simply, it's about solving problems with numbers.

Taleb, author of Black Swan, said: "Mathematics is not just a 'math game', it is a way of thinking. ”

If the average depth of a river is 4 feet, never cross it."

meters is the average depth, maybe the river is only 10 centimeters deep, and the river ** is two meters deep. This is the same as financial risk.

Hubbard, the founder of Applied Information Economics, pointed out the key to quantitative thinking in his book "Data-based Decision-making": "The concept of quantification is to 'reduce uncertainty', and it is not necessary to completely eliminate uncertainty. ”

Hubbard said, "The quantitative approach is hidden in the quantitative goal. "As long as you figure out what the metrics you really want to quantify, how to quantify them is natural.

The key to quantitative thinking is to be aware of what should be quantified. Even without precise data, we can solve some of the seemingly unsolvable problems in life.

This type of question is the famous Fermi problem, the most classic of which is: How many piano tuners are there in Chicago?

Fermi's estimation with quantitative thinking:

Let's start with the following assumptions:

About 2.5 million people live in Chicago.

For every 100 people, there are about 2 pianos.

The piano needs to be adjusted once a year.

It takes about 2 hours per tuner to tune a piano.

Each tuner works 8 hours a day, including 2,000 hours a year on the road, so a total of 1,600 hours a year.

These figures, all of which are gross estimates, are very inaccurate. If we simply calculate that there are 50,000 pianos in Chicago every year, and then calculate the working hours of tuners, we can estimate that there are 63 tuners in total.

In fact, there are about 83 piano tuners in Chicago. It can be said that it is very close to the estimated value.

Those high-tech companies in Silicon Valley want to test whether you have ideas and solutions to solve problems that you have no clues.

Quantitative thinking can make uncertain problems gradually clear. The focus is not on calculations or the pursuit of accurate data, but on grasping the key points and selecting the indicators that need to be quantified.

1.The best scenario for the application of quantitative thinking is to do t in **, and withdraw after reaching the goal every day, which has accumulated a lot of money in the short term, but this is contrary to the right of time and probability. As far as ** investment is concerned, quantification is to reduce uncertainty and obtain or achieve the required goals within a certain range, while time weight is also to formulate quantitative indicators in a large dimension, but is there a conflict between the two?

Of course, my perception is limited at present, after all, this thing is difficult to grasp.

2.Mathematics is my weakness, and I have been more exclusive since I was in school, but really everything can be counted, and it is more of a pioneering way of thinking, and every choice will have 50% of the results and failures, so what about multiple choices? Let yourself be like an ant, calculate first and know by heart, perhaps, your best choice will come to life.

3.In the face of random regret such as sex is not surprised, which requires a strong psychological receptivity, or the ability to calculate, you must have predicted the results of this choice before making a choice, each result is different from your tolerance, then the uncertainty is reduced, in this case, with the predicted answer, go to Biqi to face randomness, this is a kind of predicted "surprise".

Quantitative thinking is the process of thinking and behavior in which the brain analyzes the physical and chemical change process of unknown things and establishes the balance between characteristic parameters under the action of quantitative psychology.

The Importance of Quantitative Thinking:

Although I had the awareness of quantitative thinking before, I suffered from my lack of execution, and I was sloppy when I made plans, but it wasn't until one day that I read the book "Chu Shijian's Biography" that I felt more and more how important it is to have quantitative thinking.

The protagonist of the book, Chu Shijian, is a person with strong quantitative thinking, and when he roasted wine when he was a child, how much wine would be made from each pot of corn? How much firewood is needed? How high is the temperature?

What is the heating time per time? These are clearly calculated, and in this way, the yield of his roasted wine is higher than that of the master, and the quality is still very good.

Later, he became the director of the Yuxi Tobacco Factory, and in order to improve the output and quality of the cigarette factory, with his quantitative thinking, he led the Yuxi Tobacco Factory to become the No. 1 cigarette factory in Asia at that time.

When he was old, he planted oranges, how many trees did he plant per acre, and how much fertilizer did each tree need? He calculated how many leaves he needed to cut, so much so that the Chu oranges he grew became a well-known local brand.

Chu Shijian's life is extraordinary, his business acumen is unmatched by ordinary entrepreneurs, I think he can achieve so much, and his quantitative thinking is inseparable.

Recently, I signed up for some online paid courses, about growth, about financial management, etc., these two should be related to a person's lifelong events!

Last night, there was a phrase in the financial management class: quantitative thinking!

The first time I came into contact with quantitative thinking, I was really confused, but literally my understanding is quantitative thinking.

So what is quantification?

The encyclopedia is explained from two aspects: the digital realm and the life realm. In everyday life, quantification means that a goal or task is specific and can be clearly measured. In the digital realm, quantification is digitization.

Quantitative thinking can be used not only for financial investment, but also for all aspects of daily life.

For example, if you go shopping to buy clothes, the 100 yuan clothes are trendy, the quality is okay, it looks cheap, and you can afford it at any time, but in fact it can only be worn for one season, and it is more likely to be worn once and then pressed to the bottom of the box; And 1000 yuan clothes, the quality is very good, at first glance it looks expensive, but it can be worn for several years, and can bring you a lot of good impressions of others about you, such as "tasteful", "exquisite" and "professional". In this way, by thinking and comparing quantitatively, you can know that the garment is more worth buying and owning.

In fact, buying an expensive dress with a classic texture is equivalent to investing in yourself and making yourself more confident and attractive. In many cases, the cheapest is often the most expensive. So, learn to make choices and take a long-term view.

That's one of the things that quantitative thinking has taught me.

Quantization is to discretize the amplitude of the instantaneous value obtained by sampling, that is, to use a set of specified levels to express the instantaneous sampling value with the nearest level value.

Or the range of continuous variation of the amplitude of the input signal is divided into a finite number of non-overlapping sub-intervals (quantization level), and each sub-interval is represented by a definite value in the interval, and the input signal falling into it will be output at that value, so that the continuous input signal becomes an approximate signal with a finite number of discrete value levels.

The difference between adjacent quantization levels is called the quantization order, and any analog sample value that falls within the range of no more than half of the previous or next quantization order is greater than or less than a certain quantization level, respectively, is expressed by the quantization level, and the difference between the sample value and the quantization level is called the quantization error or quantization noise.