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The Investor's Podcast (We Study Billionaires) - The Investor’s Podcast Network
TIP740: The Great Mental Models Part 1
01:15:07
Kyle discusses the power of mental models, how they sharpen our thinking, and how they improve our decision-making in investing and everyday life.
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- [00:01]
Tip Master
You're listening to tip.
- [00:03]
Kyle Grieve
Today we're diving into one of the most powerful tools available to great thinkers and investors alike. Mental models. These frameworks are used to gain a deeper understanding of the world, make informed decisions, and solve problems. As I'll discuss, individuals who consistently make better decisions in life, business or investing are the ones who will succeed, and the ones at the very top are the people with a wide variety of models that can be applied quickly with clarity and discipline. In this episode, I'll walk through several timeless mental models that have fundamentally shaped how I think and invest. We'll explore ideas like the map is not the territory and how rigid assumptions can lead to poor outcomes. The Circle of Competence why staying in your lane creates such an edge. We'll look at inversion, which is the art of solving problems by thinking in reverse. We'll look at probabilistic thinking and how embracing uncertainty can help improve your odds and a ton more. I'll also share several personal investing stories, including a few wins and some pretty painful losses that I think illustrate how I've applied metal models in the real world. I found mental models to be easy to give lip service to. They're very handy tools and I think many people enjoy learning about them but have a much harder time implementing them into their daily thinking. I'll also go over a few strategies for listeners who want to implement multidisciplinarian thinking into a daily habit. This episode is for anyone who wants to improve their decision making, build better investment habits, and view the world through a more transparent lens, whether you're just starting or have years of experience. Now let's get right into this week's episode on the Great Mental Models, Volume 1.
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Tip Master
Since 2014 and through more than 180 million downloads, we've studied the financial markets and read the books that influence self made billionaires the most. We keep you informed and prepared for the unexpected. Now for your host, Kyle Grieve.
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Kyle Grieve
Welcome to the Investors Podcast. I'm your host Kyle Grieve, and today we'll be talking about metal models and how we can use them in both investing and in life. One can argue that winners, whether that's in life, family, business or investing, are created by those with the fewest blind spots. Clear thinking is a valuable tool for winning, but it's only part of the equation. Not only must we utilize the tools at our disposal to think better than others, however, we must also use that wisdom to gain a clearer understanding of what we should avoid. Today we're covering a book that covers that topic in a ton of detail and the book is the Great Mental Models, Volume 1 by Farnam Street. The book is part of Shane Parrish's blog, so I'll be crediting him as the author throughout this episode. I think many people in the value investing community discuss mental models as they revere individuals such as Charlie Munger, who I'm going to be referencing a lot today. And why is that? It's because Munger really popularized that concept and I think did a really, really good job of explaining why it's so useful. He credits the use of mental models to helping him achieve much of the financial and life success that he has had. However, he also thinks incredibly well, incredibly clearly, and incredibly deeply. And I believe holding Charlie as a role model to think better is an incredibly, incredibly wise choice. So let's start with exactly what mental models are. Parrish writes, mental models describe the way the world works. They shape how we think, how we understand, and how we form beliefs. Largely subconscious, mental models operate below the surface. We're not generally aware of them, and yet they're the reason. When we look at a problem, we consider some factors relevant and others irrelevant. They are how we infer causality, match patterns and draw analogies. They are how we think and reason. When viewed in this light, it becomes pretty clear that we use mental models for nearly everything in life. But then why is it that some people, such as Charlie Munger, were able to use them to solve problems such as, you know, why cope with such a successful business while others had so much trouble? I think it's because most people use a minimal number of mental models and instead of layering them on top of each other, they just stick with a couple that they know best. But if we can use a variety of mental models, it helps us understand the interconnectedness of the world around us. A good thinker doesn't use one mental model to solve all of the world's problems. There are just too many problems that a single model can't solve. This is why being broad thinker will help you solve a number of problems. Now obviously this is an investing podcast, so we're going to focus here on how each of the mental models that we cover today can help us become better investors and solve some of the biggest investing related problems that we're probably going to encounter on a day to day basis. Now Parrish identifies three failures that prevent most people from interacting with reality. The first one here is perspective. So I can examine a business and based on the reports they provide for my own view of that Business, maybe. I think it's a growing business with a healthy culture. However, if you were to view that business from the perspective of a lower level employee who has, you know, being underpaid, overworked, and seeing their colleagues getting fired, they're going to have a completely different perspective than I am. We want to align these perspectives as much as possible. Mental models help us do that. The second is ego. And this is probably the most crucial reason why it's just so hard to come to grips with reality. We spend far too much time attempting to find opinions that support our belief systems rather than disproving it. And this is because we're just afraid of our ideas failing if we put them out there. We will always feel the need to defend our ideas. And the third one here is distance. So distance refers to the gap between our decisions and the outcome. The further the distance is between the two, the easier it is to maintain our views. If we touch our hands to, you know, a hot stove, we obviously are going to get immediate feedback to avoid doing that ever again. And so we can quickly update our views. But if we get delayed feedback, then it's like trying to steer a ship where when we turn the wheel, the boat won't actually turn until 30 minutes later. Now, returning to the concept of ego, the book discusses how we often prioritize short term ego protection over long term happiness. This means that we'll protect our ego today even if it turns out to be the wrong decision in the long term. And we should really take the opposite strategy. According to Parrish, we tend to view things as either black or white rather than in shades of grey. The problem is that viewing problems as being black or white often yields an incorrect conclusion. But if we observe the world, the actual colors will emerge if we allow ourselves to be open to the possibilities. Now, the following section on mental models covers how to utilize them. So I've had some difficulty in using metal models to understand them in terms of breadth versus depth. And the fact is we probably should overweight breadth compared to depth. The reason is that we might not be able to explain something like gravity to a physics professor, but we certainly know what it is. Suppose we know the central tenets of a few of the world's laws and subjects like biology, psychology, chemistry, physics, systems mathematics. Throw in art and economics, and we can easily pick and choose when these models can be used to solve specific problems. In that case, we're probably thinking better than 99% of people out there, including specialists in those fields. Now, you'll notice that the breadth of mantle models that I mentioned above comes from a wide variety of subjects, and that's precisely what we want most. Educated individuals conduct extensive research and training in a very, very specific area. Now, while they may gloss over other areas, when it comes to problem solving, they'll generally use concepts that they understand the most. However, we must be cautious not to overuse the models that we have when they aren't the right model to solve the problem. The famous saying to the man with the hammer, everything starts to look like a nail is exactly what we want to avoid. Instead of just using a hammer and, you know, mindlessly hammering away at nails, we have to become more nuanced. If the problem requires a screw, then we need a drill to solve it. If the situation requires a bolt, then we need a wrench to rotate it. Once we understand that problems require different tools, we can evolve from thinking in black and white to thinking in a more varied color spectrum. There is an important detail to think about here though, and Munger, I think, just nailed it for us. So he said, well, the first rule is that you can't really know everything if you just remember isolated facts and try to bang them back. If the facts don't hang together on a latticework of theory, you don't have them in a usable form. You've got to have models in your head and you've got to array your experience, both vicarious and direct on this latticework of mental models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and life. You've got to hang experiences on a latticework of models in your head. So the trick is, and I can't overstate how important this is, is that you must actively use a variety of mental models so that you can understand how to layer them on top of each other. And you must be able to layer the right ones at the correct times. And this is just no easy feat. The book states that successful people will file away a large but limited set of timeless knowledge to apply across endless real world scenarios. So Parish shares his six step framework here, which I want to go over, which helps improve your ability to use mental models and make their use into a habit. So the first step here is to choose your mental model. Deliberately select the ones that make sense for you given the situation. The second is to apply them and observe once you use them, observe which ones are helpful and observe which ones are not. The third here is to record and reflect. Journaling about them can be a really, really good way to reflect on and look back at specific problems and the mental models that you use to try to solve them. The fourth here is to refine your understanding. Just because a mental model fails in solving one problem, it doesn't mean that it will fail at solving a different problem. The fifth one here is to spot models in real life. Mental models work well in all types of situations. You just have to be willing to observe them closely. And the last One here, number six, is practice. I think Munger did this automatically and 99% of people just don't do it. You must be intentional about building this habit. But as Munger has shown, it's really, really worth that time commitment. Now, from a personal perspective, I think the best approach is to consider a problem on a daily basis, then try to use one or more mental models to help solve the problem that you're faced with. You can also just kind of simulate this. You know, you don't have to use things that are happening right now. You can even look back hundreds, thousands of years at past events and try to solve whatever problems those people had using metal models. You know, Munger loved solving all sorts of problems. And I think the more practice that you put in, the better you'll get, just like Munger. Now, I've noticed that I get better personally when I practice this regularly, but if I take a break, which is really, really easy to do because I think this type of thinking requires a lot of mental energy. And if I take a break, I just end up falling back kind of to my default thinking, which tends to be lazier and much less effective. So let's move on here to the first mental model, which is titled the Map is Not the Territory. This concept relates to reality and how our map of reality is not really reality itself. All maps are imperfect. So we must acknowledge that our perception of reality will never be entirely accurate. This doesn't mean that maps are useless, far from it. It simply means that they can't be applied universally to understand reality. Now, I hope I haven't completely lost you here yet. So let me try to provide some more context to come up with maybe a bit better of a description. So a mathematician named Alfred Korshipski came up with the concept. He stated, the description of the thing is not the thing itself. The model is not reality. So his concept of maps has four parts. The first part, a map may have a structure similar or dissimilar from the structure of the territory. The second is two similar structures may have similar logical characteristics. The third is a map is not an actual territory. And the fourth is an ideal map would contain the map of the map, the map of the map of the map, etc, endlessly and so forth. So what exactly is a map? We can look to physics, for instance, for a great example. So Newtonian physics was used for centuries to help us understand the workings of our world. But then Albert Einstein came along with his theory of special relativity, bringing in the dawn of quantum physics that didn't follow the same laws that Newton had created. So you can't use Newton's map of physics and apply it to Einstein's world of quantum physics and vice versa. So let's just use an even simpler map and use the mental model of an actual map. So let's say we're using a map to maybe navigate a specific location in a foreign city. So the simple route that we look at on that map is just to take a highway to get to our desired destination. So now that we're, let's say, actually on that highway, we discover that unfortunately, the road that we plan on using is now closed due to poor weather. Now our reality has changed and that map is no longer helpful for us. But had the weather been typical, the map would have absolutely served its purposes. So how can we ensure that a map or a model is used accurately? Paresh mentions three critical aspects here. So the first one is that reality is the ultimate update. It's great to have maps in our heads, but we must be open to allowing reality to alter those maps when our map doesn't conform to reality. The third one here is to consider the cartographer. So two people can have different maps of the exact same territory. And what that means is that maps reflect things, you know, things like values, standards, and limitations of their creators. And the third one here is that maps can actually influence territories. Forcing things that don't fit reality can actually change reality itself. So the book provides a great example here of how city planners in the US Would come up with these elaborate maps for cities it designed without actually understanding how cities function. And as a result, there were several negative consequences that occurred because of this. So the mental model is really effective here because it really aligns well with certain biases that we have, such as commitment bias. So when we commit to something, it becomes increasingly difficult to change our mind on that thing that we've committed to. And I think when we create maps, we hammer into our heads that the territory looks like our maps and we limit our ability to really improve those maps when new information is presented to us. Now every investor is going to have a map of every investment that they've ever made. All the work, reading, research and channel checking you do helps build your own map. And you are the cartographer, meaning you bring your own biases, experience, associations and knowledge to map the territory. However, we must ensure that we update the maps and do not allow them to influence the territory. Let's explore this in some detail with, I think a nice story about my own investment into Alibaba. So for those who don't know what Alibaba is, it's a Chinese conglomerate that's primarily focused on E commerce. When I acquired the business and created my own map, I viewed it as a very, very great source of opportunity. I felt the company was so good that it could spawn these adjacent and non adjacent business units to really just help continue growing the business at a very, very healthy rate. And things looked really good for a time. You know, I bought this during COVID 19 and previous to when I bought it, the numbers were just spectacular leading up to 2020. So they had five year compounded annual growth rates in revenue of 50%, net income 20%. So you know, when I saw the business growing fast and trading at a PE in the low 20s, I was just all over it like a cheap suit on Warren Buffett. But as the years passed, the investment just wasn't really working out. After buying it, the company continued to sell more products and services and revenue grew at a very respectable rate of 19% per annum. But the problem was that they had this inability to improve profits. Many of the business segments were increasing the top line, but the business just hadn't figured out how to make these segments profitable. Once I felt that I had a firmer grip on reality, I decided as a cartographer of my own map, to make changes. So while the business was growing its top line, nearly all of its profitability was just coming from one segment, the China commerce segment. It was essentially carrying the whole business in terms of profitability, but even that segment was shrinking. So instead of viewing the business as a sprawling conglomerate, my map changed to one that depicted a business using profits from its crown jewel to fund other unprofitable areas of the business. And at that point, the investment was no longer appealing to me and I sold out, unfortunately at a pretty steep loss. Now, while it was painful to lose capital, I realized that the opportunity cost of staying in the business was just too high. While I think I did a good job of updating reality, eventually I probably allowed the map to influence the territory for a little bit too long. Now, I've updated my investing strategy to account for a few of the errors from this one investment. So there's four. So the first one here is that I like to revisit my investing thesis every single year, and I openly welcome and actually search for disconfirming evidence that I could be incorrect. The second one is that I set kill criteria for my business's key performance indicators. You know, if I think a business is supposed to grow at 20% a year, and a year down the road, it's decreased by 20%. Well, that's a pretty good indication that I'm incorrect and that it might be time for me to sell. So the third one here is that I use shorter time periods. When I first started investing, I thought that I could hold everything forever, just like Warren Buffett, and quickly learned that I was wrong way too often to really hold that mental model and strategy in my head for a long period of time. So now I kind of just default to a few years in advance, maybe call it two or three years, and that allows me to let go of an idea a lot more easily. And the fourth one is that I refuse to invest in countries that put the state's interest above shareholders. Now, it was a challenging, painful, and very expensive lesson, but I think it made me a much better investor. I believe this mental model has helped a lot for me in terms of the applicability in investing and in life in general. I think it teaches us that we must keep an open mind to being wrong and update our maps. When evidence supports a change in thinking, it reminds us that thinking in rigid terms is very unproductive, potentially dangerous, and unprofitable. While maps are helpful to get a grasp of the world, they need to constantly be updated to retain their usefulness. Now, the following mental model has been famous among Warren Buffett and Charlie Munger, and this, of course, is the circle of competence. It breaks down to a simple if you know what you understand, you know where you have an edge over others. It requires honesty to be vulnerable to where you lack an understanding. Now, the book covers four central tenets of the circle of competence. The first one is what is it? The second is how to identify it in yourself. Third is how to expand and maintain it. And fourth is how to operate in an environment outside of your circle of competence, which is a lot of the time. Now, every individual on the planet has limited expertise or knowledge. Therefore, there is a circle of competence that encapsulates all of the expertise and knowledge that you will have, and it will vary of course, from person to person. But it can be larger or smaller depending on whether people are intentionally trying to learn or keep themselves you know, closed off. My favorite example of the circle of competence is John Arriega. So John Arriega was a friend of Charlie Mungers who went from having no money to becoming a billionaire in about 40 years. And this was largely due to an incredibly narrow circle of competence. So he lives within a mile of Stanford University and reportedly only invested in property also within a mile of that university. So here's what Mohnish Pabrai said about John. All he did was he never put on a lot of debt and when things went down, he bought. And when everyone got euphoric, he sold. That's all he did. What is John Arriega's circle of competence? Is it real estate? No. Is it US real estate? No. Is it California real estate? No. Is it North California real estate? No. Only real estate around Stanford. His circle of competence is this small. Now the point is, as investors, there are nearly unlimited opportunities that are available out there for us. And there are many, many ways to filter things. But a simpler one might be to just simply use your circle of competence. What life experiences have you had that others haven't? What are your hobbies, interests and your vocation? All these things add up to create a circle of competence that can be leveraged to make more informed and great investing decisions. Let's take a quick break and hear from today's sponsors.
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- [22:31]
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Kyle Grieve
Expert alright, back to the show. Now let's move here to the second tenet and explore how, as individuals, we can utilize the circle of competence. So we can think of it in two ways. Number one is what do we know? And number two, what do we not know? And the answer to number two is pretty simple. We don't know a whole many things, but having the ego to admit that is a very, very potent tool to use. Now, the answer to number one is a little less clear, but Paresh, I think, does a pretty good job explaining how we can think about our own circle of competence. First, if we're inside our circle of competence, we can make decisions quickly and accurately. We will also understand if there's additional information that's required to come to a better understanding. We'll also understand that there's going to be certain information that's just unobtainable and we should be able to distinguish between the knowable and the unknowable. We also have to consider, you know, potential objections, as we've already had experience dealing with similar situations. I think it's easier to understand your circle of competence when you apply it to some of the businesses that you might already own. For instance, I don't really consider myself to be a retail specialist. I've never actually worked in retail before, but I felt like I understood the connection that Aritzia, which is a women's clothing retailer that I own, had with its customers. The brand is from the city. I grew up Vancouver, and I remember women wearing the clothing 25 years ago. I recall how they appreciated the clothing, its durability, and how it seemed like every female I knew was essentially addicted to that one brand. Fast forward to today, and I don't think much has changed on how women perceive that brand. So I had to gain a better understanding of how retail works. I had to understand the margin on the products, how they differ from competitors, and why they charge what they charge. I also began to understand some of the cyclical nature of retail businesses, how cash flows are affected in the short term by capital expenditures which are required for growth. I learned more about the seasonality of their business and how the Christmas season consistently drives a very, very strong quarter. And I could go on and on, but, you know, I had to build this out over time, slowly, and it takes time and effort. Now I feel much more comfortable looking at retailers and I've even had success investing in Dinopolska, which is a Polish grocery store that also serves as a retailer. Thanks specifically to my knowledge in Aritzia, although they operate in different industries and in different parts of the world, gaining insight into retail operations from my experience with Aritzia proved extremely helpful when I invested in Dinopolska. Now, I've already touched on how to build and maintain a circle of competence with the Aritzia example. However, let's delve a little bit deeper into the concept of building and maintaining a circle of competence so you can think of your circle of competence as a muscle. If you train it, it grows, develops, and it gets stronger. The circle of competence is the same thing. It needs to be constantly trained. It's a dynamic tool. Now, there are three ways to build and maintain a circle of competence. First, you have to be willing to learn and expand it in the first place. Many people just never read a book or learn anything after they graduate from university. This is a surefire way to never improve the circumference of your circle of competence. And the second is to monitor your track record regarding your circle of competence. This is pretty easy in investing. You just look at some of the significant losses or misses that you made and why you made them. Then you can just ignore that industry so you don't make the same mistake again. Or you can double down and learn more about that specific area to improve your weaknesses. And then lastly, you might just want to elicit some third party feedback. This can be a family member, friend, investor, or a member of an investing community. Ask them about a topic or industry and observe during the conversation whether you feel you have an intelligent conversation with that person or if it feels like you're just drowning, which will help identify areas where you have weakness. And then most importantly, stay up to date on things that you just enjoy learning about. You know, new technology ensures that hitting the pause button on a specific subject might cause you to significantly lag on specific subjects that you maybe once felt very knowledgeable about. So keep up by reading or listening to others on topics that you find very fascinating. Now let's discuss a controversial aspect of the circle of competence, which involves operating outside of it. This is an area that I think I have a lot of expertise in because I don't know much about anything. So I've had to teach myself a lot of things and in the investing world just to feel like I'm competent enough to invest in it. One key to operating outside of your circle of competence is to ask yourself a very simple question. Am I capable of understanding this As a corollary, you might ask if you're inclined to understand it. Sometimes there may be investments in an industry that you either find very, very boring or even repulsive. While it may be simple for you to understand it, if you put in the effort, you might just have zero inclination to learn about it, which signals that you can take a pass on that business. But if the answer is yes, you'll probably end up going down a rabbit hole. For me, that means doing things such as reading a company's financial statements, their annual reports, presentations, company websites, reading about their products and services, and reading other analysts as well reports on that business. If I feel reasonably comfortable, I'll start by speaking with management and chatting with others familiar with the business, industry or customers. Then I'll start looking a little more closely at competitors. This can take months, but investing is a long term pursuit, and even if you spend a lot of time on a subject that doesn't end up being an investment today, it might open up additional opportunities in the future. I think the longer that you own a business, the more you understand some of the subtle nuances that newcomers won't. And unfortunately, there isn't much of a shortcut here, which is why I think that you need to have some skin in the game to ensure that you stay hungry for more knowledge on a specific company. I notice that once I own a business I'll go the extra mile and do that extra work compared to a business that I own 0 shares in. Also, once I own something, I notice I like tracking some of its competitors a little more closely. This further improves my circle of competence by linking what's happening in one business to another and helps me see if a company is clearly outperforming another. Once you understand your circle of competence, real progress comes from thinking within it, and one tool to leverage your ability to do that is first principles thinking. Instead of reasoning by analogy, which is what most people do, or by copying what others are doing, we can start breaking down things into their fundamental truths. This method, stripping an idea down to its core components or first principles, will help you innovate, simplify and deepen your circle of competence. Let's have a quick look here at some of the simple thinking systems that Amos Tversky and Daniel Kahneman outlined in their excellent book Thinking Fast and Slow. The gist of the book is that we have two thinking systems. System one is lazy and tends to come to conclusions quickly and often relies on intuition. System two requires thinking, but it can lead to higher quality conclusions. However, it also requires more mental effort. And because humans strive for efficiency, we often rely too much on system one thinking, which can suffice, but by no means is an optimal way to solve complex problems. As a result of this over reliance on system one thinking, we tend to reason by analogy. Analogies allow us to lean on our system one thinking. However, if we want to think more deeply about problems and come up with novel solutions, we need to try to break things down into their fundamental truths, which requires system two thinking. The book states that to accomplish this, we need to find elements that are non reducible. Let's use a very simple example to help illustrate this. Elon Musk, I think, is a great example of someone who very intentionally thinks in first principles and has had incredible success, of course, while doing so. So when Elon was thinking, for instance, about space, he wanted to understand why traveling to space was so expensive. The first thing to look at was just the rocket ship. If he reasoned by analogy, or just copied others thinking, he would have realized that rockets are ridiculously expensive. If you couldn't come up with large sums of money, then you'd have to live with the fact that you're going to be paying, you know, $60 million or more for one rocket from, you know, Boeing or Lockheed. However, Musk began mentally disassembling the rocket ship to understand why it was so expensive. Was the actual ship expensive, or was there maybe something else involved with increasing the cost? So Musk examined the raw materials that were necessary to build a rocket. In order to build a rocket, you need raw materials such as aluminum, titanium, copper, carbon fiber, among many others. Now, when he calculated the cost of all these inputs, a light bulb just went off in his head. He found that the cost of raw materials accounted for approximately 2% of the price of a rocket. So the high costs involved in building the rocket weren't necessarily due to the price of raw materials, but rather to the design, manufacturing, and assembly of the rocket. Musk then focused on improving those aspects of building the rocket and was able to create them for a fraction of the cost compared to other manufacturers. Now, let's break down this process a little further. So Parrish has two separate techniques that he uses to establish first principles. The first one is what he calls Socratic questioning, and the second one is called the five whys. So Socratic questioning requires you to arrive at a very well established truth to deepen your own understanding. So first you have to clarify your thinking and explain the origin of your idea. Then you want to challenge these assumptions. Next you want to look for evidence that supports or refutes your assumptions. And then as you gather evidence, you're going to consider alternative perspectives. And if you find that your assumptions are wrong, you got to think about the consequences of your current assumptions and maybe what you could change to adjust to real. And finally, just question the original questions and draw wisdom from your research. The five whys are very simple. You simply ask why until you land on a what or a how. So Parrish writes, if your whys result in a statement of a falsifiable fact, you have hit a first principle. If they end up with a because, I said so or it just is, you know, you have landed on an assumption that may be based on popular opinion, cultural myth or dogma. These are not first principles. So let's use the five whys to identify a first principle in investing. And just keep in mind here, it's called the five whys. You don't actually have to ask, ask it five times, maybe it's three times, maybe it's 10 times. So the first question I ask here is just that, why does a business become more valuable over the short term? And the answer to that becomes because investors bid up the price of a stock. And so the second why there is going to be why do you stock investors bid up the price of a stock? Because the value of a company is below its cost. Now the third question is why would a stock's value be below its price? And it's because investors tend to be emotional. And the fourth question here is why are investors emotional? And I get two because it's human nature. And that's kind of the first principle here, that human nature in general causes stocks price to change over the very, very short term. And so from here we get to a point I think is non reducible. And therefore that's the first principle in this exact instance in investing, stock prices are determined again mainly by humans natural tendency to be emotional. So a few other additional first principles in investing that I think are non reducible are stocks represent fractional ownerships of a real business, risk is a permanent loss of capital, and long term growth of a business is driven by growth in cash flows. Now another application of first principles thinking is to apply it to management teams when evaluating a new business opportunity. Amazon is a business that I think comes to mind where I think it's quite obvious that Jeff Bezos was using first principles to build Amazon. He knew he wanted to disrupt how people bought books while working on Wall Street. He even ordered books online to see what kind of shape they would be in when they arrived. And he was thrilled to see that the book that he ordered was just in horrible shape when it arrived at his business. He thought that even the few competitors that were out there were just doing a horrible job. So instead of copying what Barnes and Noble was doing in other retail bookstores, he decided to ship books out and make the entire US market, rather than just an area surrounding a new store, be his market. And it worked, obviously, incredibly well. Then, you know, he continued using his first principles to disrupt adjacent industries. If he had just decided to copy whatever competitors were doing or, you know, shopped based on how everyone else shopped, he never would have come up with these innovative ideas that Amazon has today, such as one click ordering, two day delivery, and even ebooks. So one could argue that coming up with all the innovative ideas that Bezos and Musk developed to build their businesses requires a great deal of imagination. And I would completely agree on that assumption. This leads well into our next model, which is thought experiments. So Parrish defines a thought experiment as a device of the imagination that's used to investigate the nature of things. It's a very simple mental model, and anyone who has curiosity and imagination uses them regularly. Children are likely to be the masters of this mental model. But as we age, our imagination starts to wane and our use of thought experiments declines quite substantially. But it doesn't have to be that way. Anyone who adheres to the scientific method can really participate in the use of thought experiments. Here's how you do it. So the first one is just ask a question. Then you conduct background research. Then you construct a hypothesis. After that, you test that hypothesis with thought experiments. You analyze the outcomes and you draw conclusions. And then finally you just compare the hypothesis and you make adjustments accordingly. Albert Einstein really popularized the use of thought experiments. After all, it was Einstein who coined the term imagination is more important than knowledge. How did he use imagination in the sake of science? Let's take a look at one problem at a level that I'm comfortable with, which is to examine from the perspective of a five year old. So Einstein had a thought experiment involving a train and two bolts of lightning. So let's imagine that we're sitting in the middle of a very long train. We're staring out of the window on a rainy evening. All of a sudden you hear a boom at both the front and the back of the train as the sky just lights up around you. Now, a friend of yours simultaneously is looking at the train with binoculars and immediately calls you after the lightning hits the train. You tell him that you've never seen two bolts of lightning hit at the exact same time. Your friend seems confused. He tells you that he saw lightning hit the front first and then a split second after it hit the back. But it definitely did not hit at the same time. Now, this thought experiment helped Einstein observe that simultaneous actions were actually relative. Two observers can view the same event and actually disagree on its timing. I think thought experiments are vital for just one very impactful reason. Parrish outlines that thought experiments can be used to reimagine history. However, I think you can also use them to envision different scenarios for the future. For instance, if you're thinking about a new long term idea, you may become full of bullishness on a concept. Let's imagine that we find a business with super high recurring revenue, but also a significant portion of that revenue comes from a search engine such as Google. So bears are going to argue that since Google is being used less and less as a search engine, the business that derives revenue from Google will probably receive fewer visits on the website, which are necessary for them to maintain or build their annual recurring revenue. Luckily, this business has just acquired another growing company to complement its existing operations, which doesn't require Google to maintain or grow. Now let's use the thought experiment to come up with a bear base and bull thesis. And we'll use the scientific method that Parrish outlines. So the first question here is, is the business an attractive investment? The second question is to conduct our research, we're going to look at the fundamentals of the business. We're going to assess management, look at its TAM and evaluate the downside. From there we build our hypothesis. Let's say maybe initially we're bearish on the company as we think that artificial intelligence is going to kill Google's algorithm and disrupt this business. Fourth, we test it with thought experiments. We examine the impact of a shrinking part of the business that's exposed to Google. Also, while assessing the growing parts of the business, then we just conclude. So with all of the work that we put in, we actually conclude that our bearish stance was actually not justified. While obviously there is a part of the business that's unlikely to grow significantly, they still have alternative growth levers to pull to keep the business afloat and actually growing. And the acquisition that you initially maybe thought wouldn't have been as big of an impact on the overall revenue mix was actually incorrect. It appears that it will quickly become the major share of revenue and that segment has even higher profit margins than the legacy segment. We then need to examine how quickly the new segment will grow and whether margins can improve further. And with that, we've just done a thought experiment of sorts. You know, sure, we may have to record some numbers, but we don't know if our experiment is going to play out in reality. We just have to kind of play the odds. An analogous use for the thought experiments is to use them to consider what could happen in the future. So Sleep and Zakaria, who gained significant popularity through their feature in William Green's book Richer, Wiser, Happier, have made destination analysis a popular topic that I very, very much resonate with. And destination analysis, at its core is imagining the future of a business and what that might look like. That is basically a thought experiment. You might use destination analysis to determine how a business reaches this destination, then just monitor the business to ensure that it's moving either towards or away from that destination. For investors seeking new investing opportunities. You can actually use this as a really good filter. Ask yourself, you know, is it possible to see where this business is going to be in 10 years? And if your answer to that is no or I don't know, then you can just use that as a filter to skip the business. Thought experiments are an incredible tool for exploring ideas without the need for physical evidence. By imagining different scenarios, we can test assumptions, isolate variables, and clarify first order effects. But to truly challenge our thinking and make even better decisions, we need to go one step further. And this is where the next mental model, second order thinking, comes in. While first order thinking examines the immediate results of an action or an event, second order thinking compels us to consider the long term and less obvious consequences that follow the natural progression from asking what happens next to and then what? To understand the power of second order thinking, all we need to do is look at the pernicious effects of thinking in the first order. Parish mentions a really, really good example. So, during British colonial rule in India, the British government began worrying about the number of venomous cobra snakes that were in Delhi. Now, to reduce the count of these venomous snakes, they decided to reward people for every dead snake that they brought to government officials. And it worked. They acquired a ton of dead cobras, but they got this result specifically because Indian citizens began breeding snakes just to slaughter them to pick up a check. Now, the second order effects were that the snake problems actually ended up getting even worse. I'm gonna borrow a lot from Howard Marks on this one because I think he does a really, really good job of discussing second order effects in his legendary book the Most Important Thing. Is it a coincidence that Marks put the second order of thinking as the first chapter in his book? I don't know, but perhaps he put it there because it's just such an important concept. Now, Marks has a few key concepts on why second order thinking is so important. First, it just helps you think better. If all you think about as an investor is what's happening in the world that can make me money, you're thinking in first order. I had a social hour with the Tip Mastermind community just the other day, and one of the members brought up a very insightful point about cyclical businesses. If someone were to use first order thinking, they would buy a cyclical business at the exact wrong time. So what often happens with cyclical businesses is that they actually appear to be the cheapest at the top of the cycle, but in reality, they're the most expensive. So let me break this down. So let's look at US Steel. So in 2021, the stock traded at a price to earnings ratio of just 2 after its market cap had nearly doubled. So investors looking for cheap stocks, or maybe screening, who had no knowledge of how cyclicals might work, might have seen this, bought the stock and expected to make a decent return. And those investors would have seen the EPS decline from $15 to $0.43 today. Now, an investor thinking in second order would have looked at US Steel at a PE of 2 and then asked, and then what? Perhaps they would observe what has happened in US Steel's history and note that every time the PE got low, the business would experience multiple years of decreased earnings. They might then decide to wait for U.S. steel to have severely depressed earnings and a high PE to make an entry into the stock. Today the EPS is, you know, like I just said, 43 cents and the PE is 128 times. And interestingly, the last time U.S. steel had a PE greater than 100, the stock more than doubled less than a year later. Now, another concept that Marks loves about second order thinking is that most people just don't engage in it. And because of this, if you think in second order, you can create a major competitive advantage by having a contrarian view on a stock or an idea. Marx believes that superior results come from these non consensus views. Non consensus views arrive when you think in the second order. Therefore, Marks believes that thinking in this manner is the key to getting superior investing results. Now I can really see why Marks loves second order thinking so much. He's obviously made a huge name for himself by understanding market cycles. At such a deep level, and I think many of his insights have come specifically from thinking in second order. To understand cycles, you must examine the second order effects of phenomena such as the credit cycle. And if you look at credit cycles, things begin to change drastically when interest rates rise and fall. Different things happen when money is cheap versus when it's expensive. Most investors tend to just pile into stocks when money is inexpensive, as bonds don't offer a high enough yield and stocks benefit from increased earnings due to lower interest rates on their debt. While this cycle can take a long time to turn, it will eventually do so. And if you're all in on expensive stocks at the top of the cycle, you're in for a world of hurt once that cycle reverses. Let's take a quick break and hear from today's sponsors.
- [45:33]
D
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- [49:06]
Kyle Grieve
A true contrarian and second order thinker will happily deploy as much capital as possible when the market is most fearful. That's because they're betting the market is pricing in as much downside as possible and that the future upside going to be highly lucrative. Now Marx makes a good point that it's really impossible to predict when a cycle return. Nobody can do that with 100% precision. The best we can do is play the probabilities and just observe where we are in a cycle. Now this leads very well into our next mental model which is probabilistic thinking. Probabilistic thinking is the best tool we have to make sense of a highly dynamic and unknown future. Probabilistic thinking allows us to estimate the likelihood of a specific outcome using both math and logic, and we can really use this on everything. But one example might be to look at your chances of you know dying by some sort of extreme scenario. For instance, flying in an airplane. I can admit myself, sometimes when I'm on a plane and things start getting a little rocky, I get a little freaked out. But the interesting thing about probabilities is that we often get scared about things that have a very, very low probability of actually occurring. For instance, the chance of dying in an airplane crash are.00004. That's four zeros followed by a four. So it comes out to about 1 and 2.5 million. I would say many people fear flying more than fear driving. And yet the odds of dying in a car are actually 1 in 93 or 0.01%. So there are three areas of probability that we need to examine to really make the best use of it. The first one is Bayesian thinking. The second one is fat tailed curves. The first and third is asymmetries. So the way I like to think about Bayesian thinking is that we must use prior knowledge to come up with the best probabilities. Then, as we learn more, we update those probabilities. Whenever I update my evaluation of a company, it's usually on a quarterly or annual basis and I track three potential outcomes, a bear, a base and a bull thesis. I'm going to be using this throughout the segment on probabilistic thinking to help get some of my points across. Let's say I sign a 33% probability of the bear thesis happening on company ABC. The business, let's say, imports goods from China and then resells them across the United States. Now, given what has just happened in the United States regarding US China relations, the chance of the bear scenario happening need to be updated. I would then maybe increase my bear probability upwards. How much? There's no real right or wrong answer to that. Maybe you make it 35%, 40% or even 50%. Then you'll have to reduce the base and bull case because you can't have the three scenarios equal over 100%. As a result, my probability weighted evaluation of the business would go down. Let's now look at fat tailed curves. So bell curves are a statistical method to view the frequency of things from, you know, things such as height to test scores. You might remember from university that some teachers grade on a bell curve, meaning that the majority of students would receive the average grade and then a smaller subset would receive higher and lower grades. But fat tails are a lot different in a bell curve. The extremes are highly predictable. You might only get one student who gets an A, for instance, there isn't much deviation away from the mean or the average. But in a fat tail, extreme events significantly change the shape of the curve, which is why they're called fat tails. Instead of a bell, they might look more like a rolling mountain. This is because you might get extreme outcomes on either side lengthening those tails. Let's get back to the bear base and bull scenarios I was discussing before. So I've heard from some investors that the future is so unknown and unpredictable that maybe we should consistently overweight our bear scenarios to account for this. And to be honest, I don't really have much to argue against this other than that humans just tend to be optimistic and it might be a little bit harder to do that. But let's say the investor looking at company ABC was a real pessimist at heart. Perhaps while analyzing the company and reviewing its evaluation, they identified some fat tail events related to the transition of a new administrat administration to power the U.S. as a result, he ascribed the bear thesis of the business already to 40%. Now, since he'd already accounted for things like potential tariffs, the initial probabilities don't need to change that much. Perhaps he's increased it to 42%, but nowhere close to say a 10 to 15 or even 20% increase now. I think it's pretty tough to feel this way, although it would be highly beneficial to do so. Warren Buffett in one of the Berkshire Hathaway annual meetings once said, we think about worst case scenarios all the time and then we add on a big margin of safety. We don't want to go back to go. So we undoubtedly build in layers of safety that others might regard as foolish. But we've got 600,000 shareholders and members of my family have 80 or 90% of their net worth in the company. I'm just not interested in explaining to them that we went broke because there was a 1 in 100 chance of that happening. Even if the remaining probability was maybe a chance to double our money. I decided it's just a gamble not worth taking. We're not going to do that. It doesn't mean that much to us. We are never going to risk what we have and need for what we don't have and don't need. We'll find things to do where we can make money, but we don't have to stretch to do it. It's my job and Charlie thinks the same way. We don't even have to talk about it that much. It's our job to figure out what can really go wrong with this place. We've seen September 11th, we've seen September of 2008, and we'll see other things of a different nature but with similar impacts in the future. We not only want to sleep well on those nights, we want to be thinking about things to do with excess money that we might have lying around. Now, thinking about downside is one of the hallmarks of value investors. Unlike most other investors, value investors place a significant emphasis on the potential losses that could incur in their analysis of a company. While owning a business that can double in three years is great, if you can lose all your money, it might not make a very good investment. And as Buffett pointed out here, at least at the time he commented, he's unwilling to make an investment that can make him go broke even when the probabilities make sense. Now this scenario is actually really interesting that Buffett discusses because what he's talking about isn't actually a horrible bet if you look at it in terms of expected value. So for instance, okay, let's say that he has an opportunity to invest $20 million. There's a 1% chance of going to zero and a 99% chance of doubling. So the expected value of that bet is actually $39.6 billion. However, due to the fat tail events that Buffett has experienced, he would remain unwilling to make that bet. So this really involves examining fat tails and taking precautionary measures to avoid such events. The problem is that some fat tail events are just inconceivable to pretty much everybody as Buffett discussed above. You know, 9, 11, the great financial crisis were not events that I would assume that 99.99% of analysts were accounting for before they happened. Now the final aspect of probabilistic thinking is asymmetries. This reminds me of a tendency towards excessive self regard. We tend to overestimate our skill level. Let's go back to the same example on Company abc. Now let's say we have a newer investor who maybe thinks they're going to be right well over 50% of the time. In this case, his bear probability is only 20%. And despite the tariffs that the US has imposed on China, he has so much excessive self regard that he refuses to update his probabilities once the US and China are entrenched in some sort of trade war. In that case, he's probably going to lose a lot of money. It may not go to zero, but the likelihood of him losing money is going to be way higher than the first two examples that I gave. Now what works really well in investing is to think in base rates. You can use the market as well for your base rates. The market indicates that the average long term returns for a stock are approximately 9%, so if you are underwriting returns above that, you have to give yourself some room to fail. This has been an excellent lesson for me. I've gradually increased the probability of my bear cases up to around 33% by default as a result, and even this might not be high enough. So since I have certain businesses in my portfolio that I can hold for multi year time periods, I can adjust each of my scenarios as more information becomes available to me. The key for me is to ensure that I'm not being overly optimistic. Just like I was saying earlier, it's important to remember that the next fat tail event is somewhere in the future. So painting too rosy a picture can really get you into a lot of trouble if you're too optimistic. Now I'd like to close out this part on probabilistic thinking with a great quote by Shane Parrish in the conclusion of the chapter. So successful investing in shades of probability means roughly identifying what matters, coming up with a sense of the odds, doing a check on our assumptions, and then making a decision. We can act with a higher level of certainty in complex, unpredictable situations. We can never know the future with exact precision. Probabilistic thinking is an extremely useful tool to evaluate how the world will most likely look so that we can effectively strategize. Thinking probabilistically requires a creative mind. When we are imagining scenarios for our business, we will be guided by optimism and we will want those optimistic scenarios to play out. But if you invest long enough, you'll realize very quickly that not everything is just sunshine and rainbows. You're going to be wrong, and probably more often than you might be comfortable with. For instance, I track my hits and misses to better understand my own base rates, which I can then use as data to better understand how often I'm right and wrong. A hit for me is when a stock provides greater or equal than 0% returns and a miss is when a stock returns less than 0%. So as of Q2 2025 I'm hitting on about 57% of my picks and I spent a lot of time in the past in the 60s percentage wise, and so hopefully I'll get back to there with my current portfolio. But we'll see. So my misses are where I like to spend a lot of my time because I want to learn how to avoid them. And one way I Imagine a business becomes a miss is by using the mental model called inversion. Inversion was popularized heavily by Charlie Munger, who Learned it from 19th century mathematician Carl Jacoby. Jacoby solved some very difficult mathematical problems, starting with the endpoint and working backwards as a part of his strategy. He continued to work backwards to come to some solution. And this is the essence of inversion. Regarding investing, Charlie has said it's bad to have an opinion you are proud of if you can't state the arguments for the other side better than your opponents. This is a great mental discipline. Charlie used inversion to improve his knowledge on a subject and didn't believe you should have a stance on an issue if you couldn't argue both sides. I think this is just a great model to live by. Munger also has a quote that really resonated with just how robust inversion is. Instead of looking for success, make a list of how to fail. Avoid these qualities and you will succeed. There are so many ways that I've thought about inversion, both in investing and in life. So let's examine both. So in life I like to use inversion to do precisely what Munger suggests above, which is to avoid failure. One thing I think about very often is just how to be the best father to my son. And while I can imagine all the great ways to go about this, I can also think about all the ways to just be a bad father. Then I just simply avoid those failures and hopefully that'll make me a better father. So what might that look like? I think bad fathers have a couple similar themes. So the first one is they're unpredictable. The second is that they deprioritize their children. Third one might be that they're absent. That could be mentally or physically. The fourth is that they don't create memories. And the fifth is that they pass on poor values. So with this in mind, I'm just always trying to avoid these. Some of them are easier to avoid than others for me, but I think it just does a really good job of helping me identify areas where I can maybe improve. While I know I'll never be perfect, I can strive to be the best father that I can be, using inversion and just working on my weaknesses while maintaining or building my strengths. Now, for investing, I prefer using inversion in two different ways. So the first is in my investing philosophy. So there are decades of investing resources and role models to observe good actions versus poor actions. And I think it's pretty easy to find out the actions that have caused some very inferior outcomes, specifically from other investors. We could just use These events to learn vicariously through others, to try and save ourselves from making mistakes and saving ourselves from losing money. So what is it that investors do? Whether that's fund managers managing billions of dollars down to the retail investor buying a fractional share of Amazon. So here are my best guesses. The first one, leverage up your trades. Second, ignore history. Third, focus on the upside exclusively and don't bother at all with the downside. Fourth, focus on using market prices to determine decision making. Fifth, ride momentum. And sixth, and maybe most important, ignore value. So knowing this makes investing a lot more simple for me. For instance, the leverage part is easy for me to avoid. I've never done it on stocks and I have no plans to start. And I love learning about history, so that's also an easy one for me. Number three is one that I try to do well on, but I know is a problem that I can maybe succumb to. Numbers 4 and 5 are problems that I think all investors have. And the trick is in the degree to which it affects us. I think a lot of retail investors focus too much on momentum. They might look at a business at a 52 week high, then just buy it. Then if the market continues to carry their stock, they give themselves a pat on the back. But if the market no longer likes it and investors leave that name, they're going to end up selling it as they allow market prices to determine their decision making, even if it might be cheap. Which leads to point six. Here you have to focus on things like price and value. There are businesses where the market misunderstands things. It, you know, happens all the time. However, if you don't understand the gap between price and value, you're going to make poor decisions when the price changes drastically on you. And if you don't know the price and value of a business when that price falls, you have no system to help guide your decision making. And for many investors, the default just becomes sell. Now the next part of inversion that I like to use is on a company specific basis. So whenever I examine a business, I want to understand how I could potentially destroy that business. If I know how to destroy a business, I can tell a couple things. First, I can assess whether a company is easy or challenging to kill. And generally speaking, you know, a business that is easy to destroy is going to have a weaker moat compared to a business that is, you know, more difficult to kill. Once I have a general idea of how to destroy a business, I can then track it. I track the businesses inside of my portfolio very closely. So I want to see if There are specific KPIs or fundamentals of the business that are starting to crack. You know, I might ask which of these breaks fits my narrative of the business starting to get destroyed by the power of capitalism. Sometimes declines in KPIs are nothing but noise due to something like a rocky quarter or maybe some sort of short term event that happens to even the best of businesses. The hard part of investing is figure out what's signal and what's noise. There isn't much to add here other than that. If the business is within your circle of competence, you should have a very good understanding of some of the fluctuations that you're gonna see in a stock's price. And you should be able to delineate between signal and noise. But you obviously always need to be on the lookout for true signals because some noise can masquerade itself as a signal. So the thing I love about inversion is that it really helps us understand that we don't necessarily need to have 200 IQ points to succeed. Much of the success in everything, not just investing, is just about avoiding failure. And inversion is one of the best ways I've come across to think specifically about how to avoid it. If you can live a life with minimal failures, you'll end up with a very fulfilling and successful life. And investing, if you can do the same, you'll have a lot more money in the future if you can avoid failures along the way. Now, the next concept I want to cover here is going to be Occam's Razor. So Occam's Razor states that more straightforward explanations are more likely to be accurate than complicated ones. Since we've spoken a lot about simplicity and complexity in today's episode, you can probably see why I think Occam's Razor is a great mental model to have in your thinking processes. Now, to add to the definition here, according to the book, we should focus on the simplest explanations with the fewest moving parts. It's important to understand a common misconception here. Occam's Razor doesn't say that the simplest explanation is always correct, just that it's a great starting point for rational thinking. If we're looking at a problem and are faced with two solutions, then the one with fewer variables is more likely to be the solution that we should choose. It's also important to understand that simplicity doesn't include superficiality. It's definitely not an excuse to avoid hard work. You must understand the underlying concepts. But I think you can still use it to trim away Some of the excess complexity. Occam's Razor is vital in investing when looking at new opportunities or looking at the best opportunities that are maybe already in your portfolio. For instance, I spend a lot of time thinking about the companies inside of my portfolio. I do this to think about which businesses might be the best candidates to eliminate and which are the strongest names in my portfolio, which are tend to be businesses that I want to hold for multiple years into the future. A small business I won't name is a security tower rental company with 247 surveillance. Sounds boring and low tech, but it's actually the biggest multi bagger I've ever had. Now when I think about this business with Occam's Razor, I like the business. It's simple and it doesn't require a lot of assumptions to understand why I think it's been successful so far or why I think it's going to be successful in the future. So it really comes down to three things. The business sell these security towers that have very, very slight variation. So essentially it's, you know, one product and it's very simple to use and it can easily be relocated. The second is the growth story. It's simple to me. They simply just manufacture more towers. They make sure their utilization rates are high and they keep selling and there you go. And then finally, I have to just really watch the margins on this business. I have to make sure that margins aren't going down because that would indicate to me that they're probably having to decrease prices to deal with incoming competition. And this is something that they've actually been expanding, not declining or even maintaining. So I'm hoping that they can maintain this for multiple years, but it's not something that I think they'll maintain forever. And that's, you know, really, that's really it. When you contrast this with another business inside my portfolio, a Swedish serial choir of industrial businesses, it definitely has a little more complexity. Things aren't going so well with some of its subsidiaries. So I have to monitor that. Then the problems that they are having are causing compression of margins and profitability. And then because the business also has a bunch of really high quality newer assets that were purchased by a different person than the legacy companies, I also have to consider that obviously, you know, do I stick with the margin that they have now, thinking that's probably what it's going to be like in the next few years, or do I look at the margins of the newer businesses and portray that out to the next few years? And there's obviously Additional complexity that I won't get into because it'll become an episode on its own. But you can see here that, you know, just looking at the two scenarios, simplicity just generally wins out. I obviously own both of these businesses. I like both of them, otherwise I wouldn't have them. But I would prefer more simpler businesses in my portfolio than complex ones. Now, I'd like to share a great example that Shane Parrish goes over the book, which is why are more complicated explanations less likely to be true? And let's work this out mathematically. And don't worry, I'll use some very simple numbers here. So take two competing explanations, each of which seem to equally explain a given phenomenon. If one of them requires an interaction of, let's say, three variables and the other requires an interaction of 30 variables, all of which must have occurred to arrive at the stated conclusion, which is more likely to be in error. So if each variable has a 99% chance of being correct, the first explanation with only three variables is only 3% likely to be wrong. Very high. Now, the second, more complex explanation is about nine times as likely to be wrong at about 26%. So the simpler explanation is more robust in the face of uncertainty. If we apply this concept when evaluating new businesses to add to our portfolio, it can also serve as a useful filter. If a given business requires dozens of variables for your thesis to play out, you should probably just skip it. Instead, search for businesses with a few variables that will give the business the success that you're searching for. I assume this is why Buffett preferred buying simple business models. Less variables made it easier for him to understand, and therefore he could come to more accurate conclusions that he had high levels of conviction in. I think that's something to clone. Now, Occam's Razor is a great tool to focus on simplicity. It's not perfect and can't be used for problems that require complex situations. But I think there's enough puzzles in our daily lives, especially in investing, where simplicity really is the key to success. So I highly recommend using Occam's Razor to identify the simplest solution. Now we come to the final mental model of the book, which is called Hanlon's Razor. And this one actually reminds me a bit of Occam's Razor, in that it helps us come to conclusions a little more quickly and can help take the emotional part of the equation out of things. So Hanlon's Razor states that we should not attribute to malice that which is more easily explained by stupidity or carelessness. There's a great example of the effects of Hanlon's Razor that Daniel Kahneman and Amos Tversky discussed in a 1982 paper. So Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy as a student. She was deeply concerned with issues of discrimination and social justice and also participated in anti nuclear demonstrations. Which is more probable, Linda is a bank teller or Linda is a bank teller and is active in the feminist movement. So the majority of respondents to this paper chose option two. They did this because the description given of Linda fit into the narrative of her being a feminist. So they chose option two. However, the fact is that any feminist bank teller is a bank teller, but not every bank teller is a feminist. I found this mental model to be the least useful personally out of all the ones in the book. However, I still think it's useful on a daily basis for dealing with bad emotions that we might harbor towards others. If we get, you know, splashed by a car on a rainy day, our first instinct might be that the driver did it on purpose, but more often than not, they just didn't see you. Hanlon's Razor reminds us that most lights aren't personal, they're just puddles we all step into sometimes. Now, a potential use case for investing would be to search for managers who have made significant mistakes. The market may generate large amounts of short interest in these businesses because they believe the company maybe is fraudulent. For instance, if you enjoy these types of opportunities and conclude that the short sellers are actually incorrect and that the business is just maybe going through some missteps, accounting mistakes, maybe some product recalls or working capital issues, etc. You can uncover some tremendous opportunities. Once the market realizes that the business is unlikely to be very fraudulent. So that's all I have for you today on Mantle Models. If you'd like to interact with me on Twitter, please follow me rationalmrks or on LinkedIn under Kyle grief. If you enjoy my episodes, please don't hesitate to let me know how I can improve your listening experience. Thanks again for tuning in. Bye bye.
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Tip Master
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