Source: Red and Green As centralized investors, our goal is to have a greater understanding of our companies than any Wall Street investor. If we are willing to work hard and learn as much as possible about our companies, we are likely to know more than general investors, which is all we need to gain a competitive advantage. On the product structure side, the operating income of products worth 10-30 billion yuan is 401/1288/60 million yuan, respectively, in 2023, the overall sales volume of the company reached 18,000 kiloliters, a year-on-year increase of 28.10%, showing significant growth.

Based on two simple principles. In 2023, the company's overall sales volume of 18,000 kiloliters, +28.10% year-on-year, significant growth. Product structure, 10-30 billion yuan products operating income of 401/1288/60 million yuan respectively.

Robert Hagstrom was the first person to write about "grid thinking" in his book, "The Grid Investment Mindset," in 2000, which has been around for twenty-four years. It has now spread throughout the investment community. However, fifteen years ago, we did not have any insight into the way of thinking about grid.

Charlie Munger proposed the "grid thinking" model. This concept originated from a speech Munger made in April 1994 at the Marshall School of Business at the University of Southern California. The title of his speech was "Choosing Stocks Is an Art and a Wisdom." At that time, Munger told the students that they should broaden their vision of the stock market, finance, and economics, not to view them as independent disciplines, but to see them as a larger knowledge system that includes psychology, engineering, mathematics, physics, and anthropology.

In this broad perspective, various disciplines are intertwined and developed in the process. People in various disciplines will form thinking patterns, which are the key to comprehensive understanding. People with this kind of broad vision will gain great wisdom in dealing with the world.

Munger compared this kind of cross-cutting thinking pattern to a grid pattern. Therefore, the word "grid" became a term in the investment community and a mark of Munger's investment theory. The grid is a 4x8 small square grid that can be used to decorate walls, shade in courtyards, and support vines and plants. "Grid thinking" is a metaphor that refers to in-depth learning and understanding of multi-disciplinary knowledge and the integration of the basic principles of different disciplines into a logical chain, just like a criss-crossed grid, analyzing the same problem from multiple different perspectives and dimensions. Grid thinking is to explore its complexity in the "grid," because both investment and the market are complex systems.

Munger has always believed that forming thinking patterns in mutually independent disciplines and establishing a thinking pattern grid is a powerful way for investment to succeed. When the views of other disciplines also come to the same conclusion, investment is more likely to be correct.

This is the best return. A broad vision makes us better investors, and the effect is obvious. But it also brings something more important - those who try to understand the connections between various disciplines will gain great wisdom in dealing with the world.

How can we gain great wisdom in life? This is a process of development: First of all, learn the important concepts of various fields, which is the pattern; secondly, try to understand their commonalities. The former concerns our education, and the latter is a problem of learning how to think and distinguish. There is no doubt that learning the knowledge of various disciplines seems to be an impossible task. But fortunately, we don't have to become experts in every field. We only need to learn the most basic things, which Munger calls "big ideas." By persisting in learning these big ideas, we can achieve our goals when we want to.

We like to trace the origins of great ideas. According to Hagstrom, Munger's grid thinking model is related to two people and a research institution.

Benjamin Franklin is the first person that Munger favored the most. Franklin was an outstanding politician, physicist, one of the three founding fathers of the United States, and was elected a fellow of the Royal Society of England. He was an important leader during the American Revolutionary War, participated in drafting the U.S. Declaration of Independence and the Constitution, conducted multiple experiments on electricity, and invented lightning rods. He also invented bifocal glasses and flippers.

In 1749, Franklin proposed the establishment of the Philadelphia Public College in an article entitled "Proposals Relating to the Education of Youth in Pennsylvania." His reason was that the curricula of Harvard, Yale, Princeton, and William and Mary University emphasized traditional disciplines, which were not conducive to practical subjects for young people. Franklin hoped that the Philadelphia Public College would not only emphasize classical subjects but also attach importance to practical courses.

The Philadelphia Public College is now the University of Pennsylvania. Richard Beeman, dean of the School of Arts and Sciences, describes Franklin's achievements: "Benjamin Franklin was the first to propose a modern curriculum arrangement," and "the time schedule was perfect."

The discovery of new mathematics and science in the 18th century expanded the world's knowledge base, and those classical courses like Greek, Latin, and theology could not explain this new knowledge. Franklin suggested that the college teach this new knowledge and recommended that the students acquire the necessary skills for success in their careers and public services, including writing, painting, speaking, and computing, etc. Franklin said that once students master these basic skills, they will actively acquire knowledge.

Franklin wrote: "Almost all useful knowledge can be learned by reading history." His "history" includes everything that is meaningful and valuable. He encouraged young people to read history, actually requiring them to learn philosophy, logic, mathematics, religion, government, law, chemistry, biology, hygiene, agriculture, physics, and foreign languages.

Other

"Benjamin Franklin is the founder of liberal arts education," said Beeman, pointing out that he cultivates a thinking habit. The University of Pennsylvania is a platform for lifelong learning. "Benjamin Franklin's success as an educator is based on three famous principles. First, students must master the basic skills system: reading, writing, arithmetic, physical education, and public speaking. Then, guide students in learning knowledge entities. Finally, students cultivate their own thinking habits by discovering the connections between knowledge entities."

However, in the 250 years since Franklin proposed this suggestion, we still lack the third principle: "habitual thinking" that links different knowledge entities. The grid thinking theory believes that only by using the intersection of these knowledges can we truly understand investment.

It can be seen that cultivating "habitual thinking" is the key to obtaining what Munger calls "Shanghai DZH Limited". For investors, this effort can bring huge returns. When we allow our vision to expand, we will observe commonalities in other fields, thus forming a thinking pattern. Then, one concept will be reinforced by another concept, and so on, and we will find ourselves on the right track.

III. The transformation of linkage knowledge

The second person is Edward Thorndike. Thorndike is a psychologist, the pioneer of animal psychology, and the founder of psychological linkage. Thorndike's most important study in 1895 was how human and animal knowledge came about. He first proposed the stimulus-response model, in which knowledge is generated when association-connection is formed.

In a paper published by Columbia University in 1901, Thorndike proposed that the knowledge of one field would be helpful for learning in other fields. Moreover, he believed that only when there are similar elements between the original and new fields, knowledge will be transformed. That is to say, if we understand A and find that B and A have some similarities, then we will understand B. This view believes that learning new knowledge has little to do with personal learning abilities, but has a lot to do with the presence of common knowledge points.

Thorndike's theoretical foundation is the core of contemporary cognitive science ——the "linkage" theory. Linkage theory is based on Thorndike's stimulus-response model research. Linkage theory believes that learning is a process of constantly trying and making mistakes. In this process, responding to new stimuli actually changes the neural connections between brain cells. That is to say, when people recognize familiar patterns and adapt to new information, the learning process affects the synaptic connections between neurons. The brain can link related information into a chain, and transform what it has learned into similar situations. Therefore, a person's intelligence can be seen as the number of linkages he masters.

Contemporary business leaders and scientists attach great importance to linkage, because it is the core of a powerful new information technology system—"artificial neural network". Traditional computer technology can replicate brain activity, and artificial neural networks attempt to better replicate brain activity than traditional technology.

In the brain, groups of neurons work together, called networks, each network has thousands of interconnecting neurons. Therefore, we can think of the brain as a collection of neural networks. Correspondingly, artificial neural networks refer to computer imitating the basic structure of the brain: it is composed of several hundred processors, and the processors are interconnected into a complex network.

The powerful function of neural networks enables it to be different from traditional computers. It can adjust the weight of interconnection points between processors, just like adjusting neural synapses, so that neural networks can become lighter, more robust, and even reconnect to complete different tasks. Neural networks can learn, just like the human brain. It can recognize complex patterns, classify new information into the thinking patterns, and then get connections from new information.

There is no doubt that artificial neural network is a new technology with great commercial value. Baby food manufacturers use this technology to manage live cattle futures trading. Soft drink infusion machines use electronic noses to capture and analyze odors. Credit card companies use it to detect fraudulent behavior by identifying counterfeit signatures and deviations from consuming habits. Mortgage insurers use it to estimate creditworthiness. Airlines use it to predict flight demands. One company uses it to determine what drinks passengers most want. Postal service departments use neural networks to recognize sloppy handwriting. Meteorological bureaus use it to make weather forecasts. Computer companies use it to develop software that recognizes handwriting, which is useful for sending faxes and identifying engineering drawings on cocktail napkins.

Metaphor is the foundation of innovative thinking.

A research institution is the Santa Fe Institute in New Mexico. The Santa Fe Institute is a multidisciplinary research institution. The Santa Fe Institute was established in May 1984, and it is a milestone in the history of modern science. This research institute brings together scientists from different disciplines such as economics, physics, computer science, and biology. They have a common goal, that is, to explore all aspects of complex systems—any system that has a large number of close interactions inside, from condensed matter physics to living organisms, from economics to the whole society, forming a "complexity science".

John Holland is the inventor of genetic algorithms, a professor at the University of Michigan, and the chairman of the Steering Committee of the Santa Fe Institute. He has written books such as "Emergence" and "Hidden Order". He often lectures on innovative thinking at the Santa Fe Institute. In his view, innovative thinking requires us to master two important steps: first, understand basic knowledge; second, pay attention to the use of metaphors.

The first step is what Munger calls the first part of obtaining "Shanghai DZH Limited". Connect various thinking patterns and benefit from them. The prerequisite is that you must have a basic understanding of each thinking pattern. If you don't know how each thinking pattern works and what phenomena it describes, then you can't benefit from it. Remember, you don't need to be an expert in every thinking pattern, just understand the most basic things.

The second step is to look for metaphors. Metaphor is a hidden analogy that imagines one thing as equivalent to another thing. In contemporary metaphor research, metaphor means 'cross-domain mapping in conceptual systems.' For Holland, metaphors are not only a more vivid language, but also a reflection of thought. It helps us turn ideas into thinking patterns. He also said that metaphor is the foundation of innovative thinking. Metaphors help us compare one concept with another concept that has already been mastered, so that we can use a simple pattern to describe it, so that we can master similar but complex concepts.

James Burke describes a few cases in his book 'Connections,' in which inventors found similarities between existing inventions and things they wanted to invent. Cars are one case. The carburetor is similar to a spray perfume bottle, which is also related to how 18th-century Italians studied how to use water. The spark plug invented by Alessandro Volta was originally used to test air purity, and 125 years later it ignited the fuel sprayed by the carburetor. The invention of the car gearshift was inspired by the water mill, and the origin of the engine piston and cylinder can be traced back to the pump engine invented by Thomas Newcomen for coal mine drainage. Every major invention is related to early inventions, which is a pattern that can bring innovative thinking.

'Lattice thinking' is a metaphor used to describe the supporting structure of a series of conceptual concepts. Building a thinking pattern lattice is like designing a neural network. Psychologists are easy to associate the lattice with connectionism. Educators link it to the brain's search for thinking patterns. For humanists, the value of metaphor lies in its ability to expand the scope of understanding. This is the power of the thinking mode lattice. The use of the thinking mode lattice is not limited to picking stocks. It enables us to understand the entire market - new business trends, emerging markets, currency flows, overall economic situations, and market participant behavior.

In 1996, two years later, Munger reiterated his basic theme at Stanford Law School: only those who work hard to build thinking pattern lattices and think in a connected, interdisciplinary way can achieve true and sustained success. Once this thinking pattern is established, we have weapons to deal with various situations. 'You can catch the thinking pattern that solves the problem, you just need to know it and form good thinking habits.'

4. Grid thinking with evolution

Hagstrom points out that no classic, balanced theory of the stock market can predict or even describe the events of 1987. The failure of existing theories makes competitive theory possible. The most important part between them is a more thorough understanding of the stock market and the economy from a biological perspective. This is the most important part of the thinking mode lattice. By using biology to understand well-known finance and investment, the first thing we see may be surprising movements. The process of evolution in nature is natural selection, and observing the economic selection with an evolutionary model will allow us to observe economic selection.

Evolution is the process of species evolution. Evolutionary theory believes that favorable mutations are preserved through natural selection and passed on to offspring. After several generations of transmission, small progressive changes in the species accumulate into large changes, leading to evolution. The author of 'The Selfish Gene,' Richard Dawkins, praised Darwin's 'Origin of Species' for forever changing human perception of ourselves. It has also changed our perception of other subjects, such as economics.

The foundation of complexity research is a complex adaptive system where every changing factor is generated by the system. Biologists and physiologists are very familiar with such systems, but the expert group of the Santa Fe Institute believes that the concept needs to be expanded because it has been proven that economic systems and stock market research also have complexity. They observed that the economy has four distinct characteristics: 1) small events are interconnected; 2) there is no global controller; 3) continuous adaptation; 4) lack of dynamic balance. These characteristics are very important, but they are ignored by traditional economics.

One essential of complex adaptive systems is feedback loops. Firstly, individuals in the system generate expectations, build models, and then take actions based on the predicted results of the models. However, the models will change according to the accuracy of individuals' predictions of the outside world. If the model is useful, it will be preserved; if the model is not useful, the individual will change the model to increase its predictability. Obviously, the accuracy of prediction is extremely important to investors. If we can view the stock market as a complex stable system, we will understand it better. Complex systems are a new way of looking at the world, but it is not easy to understand them. Brian Arthur, the founder of complex economics, has provided an answer by listing the 'El Farol problem.'

The 'El Farol problem' was created by Arthur. Every Thursday night in the summer of 1992, Irish musician Gary Cathey performed at El Farol Bar in Santa Fe. At that time, Stanford University economist Arthur had just joined the Santa Fe Institute. He liked the bar and the music there, so he often went to the bar. When the bar was not crowded, he had a great time, but at other times, the bar was too crowded and there were no seats, which made him very uncomfortable. Unfortunately, the number of attendees each week varied greatly, and there was no obvious pattern, so deciding whether to go or not seemed to be a question he had to answer every week. Later, he found that other people who liked the bar faced the same problem as him, and everyone tried to do things that most others would not do.

Arthur believes that the El Farol case can be called a prediction of a balanced system. At any time, there will be a model that is destined to be effective - they successfully predict how many customers will visit the bar. Conversely, models that do not predict accurately will gradually disappear. Every week, some music lovers will use new predictive data and new models. Thus, we quickly see how the El Farol case simulates Darwin's natural selection and how it logically extends to economics and the stock market. In the stock market, individual predictive models survive by competing with other individual predictive models, and feedback will cause some models to change while others disappear.

Bank of America Merrill Lynch conducts public opinion surveys every year to determine the most influential factors for stock investment, including expected earnings per share, dividend discount models, return on assets, price-to-earnings ratios, price-to-book ratios, and so on. All these factors represent models that investors use to predict stock price trends. Compared to surveys in previous years, we can see that public models have also changed over time. Bank of America Merrill Lynch's survey is a good example of how the El Farol problem is applied to the investment world. We can say that these 23 factors constitute an equilibrium system of the Coevolutionary Hypothesis. As the environment changes, some models disappear, while others revive. This is the essence of evolution, which, when applied to economic theory, can be called practical evolutionism.

This is the best example of using evolution to think about grid. Hagestron pointed out that the core of investment philosophy is to cultivate the ability to think of investment as part of a unified whole and part of the entire body of knowledge. If done well, it will have an effect no less than that of the "outstanding person effect". This is the greatest wish of long-term investors who want to succeed.

Editor/Lambor