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What is model thinking?
Scott Page, the author of Model Thinking, is a professor of political science at the University of Michigan. "Model thinking" comes from a "model thinking course" offered by the author. It was first in college, and later it became an online class. Now there are more than one million people studying in all walks of life. What this/kloc-0.0 million people want to learn is how to understand and deal with this complex world.

Teacher Wan Weigang made a detailed interpretation of this book in the elite day class (the third season).

First, we need to know what a model is.

If "diversity" is the "perspective" of observing problems, then "mode" is the "lens" of studying problems.

To understand this weapon-grade concept, we first need to upgrade our way of thinking. If we want to use advanced methods to study problems, we must standardize our thinking.

As a modern man in a complex world, he should have the ability of exquisite deduction. This is an advanced way of thinking.

If you want to use this way of thinking, you must at least master a mathematical formula. For example, if you want to know the relationship between the power of kicking the ball and the flying distance of the ball, let's first describe the relationship between power and acceleration with Newton's second law formula, then calculate the speed through acceleration and contact time, and then consider the gravity of the earth and air resistance ... This sounds very troublesome, but this set of thinking allows you to perform exquisite deduction.

A model is an abstraction of the real world. Only abstract things belong to the logical world. Deduction can only be made after the model is established.

The model must meet two requirements:

Firstly, the model includes various entities (people, organizations, objects, etc.). ) and the relationship between these entities, all of which are very clearly defined.

Second, the model can be logically deduced.

In fact, all the math application problems we did when we were students were just using models. When you know what assumptions the topic contains and what the causal relationship is, you can deduce a result mathematically.

Abstraction can be deduced. Deduction is a very powerful ability. Once the thinking rises to the height of the model, the cognition can be greatly improved, and you are not easily fooled.

For example, in the United States, both * * and the party chairman like to cut taxes. Generally speaking, the story is that if taxes are reduced, people will have more money; When ordinary people have more money, they can expand consumption and the economy will grow; When the cake of economic growth grows bigger, the government's tax revenue will increase instead-it sounds reasonable, doesn't it?

Deduce with the simplest mathematical model: if the tax rate is reduced by 10%, the economy must grow by more than 10% to receive as much tax revenue as before ... but in recent years, the highest growth rate of the American economy is about 4%, and it is a miracle that it really reaches 10%!

Of course, the real study of economic problems needs more complicated models, but the fact is that we need models, not stories.

In order to further understand the model, we need to know the cognitive pyramid.

At the bottom of the pyramid is data. Data represent various events and phenomena. For example, if you go out and see rain, this is a data. The data itself has no organization and structure, and it has no meaning. Data can only tell you what happened, not why it happened.

The upper layer of data is information. Information is structured data. The rain you see is just data, but if someone counts how much rain it rained in a province and city in China in July 2065438+2009, it is not data, but information. Information can be used for analysis and interpretation.

The next level of information is knowledge. Knowledge can organize information and tell us the logical connection between events. Clouds make rain, and the weather gets cold because of the rain. This is all knowledge. Idioms, allusions and modes of thinking are all knowledge. The model can be said to be a profound knowledge, which can explain some things and make predictions.

The top of the cognitive pyramid is wisdom. Wisdom is the ability to identify and select relevant knowledge.

You may have many models, but it is wisdom to know which model to use for this problem and dare to use this model.

From data to wisdom, this is a real cognitive upgrade. If a person muddles along, only experiences and doesn't know the summary, and only gets the data. Some people occasionally watch the news and know what is happening now, so they get information. Someone can sum up some rules from experience and learn some proverbs from books, then he has knowledge.

Why do many people say, "I have learned a lot, but I still have a hard time"? Because knowledge does not equal wisdom. If you have wisdom, what knowledge will you choose to use, you will make decisions with models, and you really dare to implement them. That's the real skill.

From data to wisdom, the higher you go, the more subjective you are. Information is already a personalized summary. Causality in knowledge is already a subjective judgment. And wisdom is an art, which mode should I choose? There is no fixed operation process.

Economists have a famous saying that "all models are wrong and some are useful." What the model says is not the real world, but the abstraction and simplification of real events. Only by ignoring many factors can we deduce the problem. The danger of doing so is that you may eventually come to a completely wrong conclusion.

Model is a subjective and abstract description of the real world, representing standardized thinking. Through strict definition and mathematical logic, the model allows us to perform exquisite deduction, so as to obtain accurate communication, interpretation, judgment, design, prediction, exploration and action capabilities. What mode to choose, one mode or several modes, is wisdom.

Model thinking is a structured and systematic thinking, which helps us to develop extraordinary insight. But this is a process of constant "cultivation".

You need to keep thinking and choose to use one or a group of models. And we will make mistakes when using it, which is inevitable, so we need to quote repeatedly.

When model thinking becomes an instinct, we will get used to it: using many models to explain and predict a complex thing together, or using one model to explain many completely different things.