We Study Billionaires - The Investor’s Podcast Network - TIP641: Improve Decision Making with Mental Models w/ Clay Finck & Kyle Grieve

Starting point is 00:00:00 You're listening to TIP. Mental models can be used from nearly any discipline to help you think better. However, many investors get paralyzed by overanalysis when they try to think in terms of mental models. After all, you have to have a full understanding of them at a surface level, and then you need to learn to layer them on top of each other when trying to solve problems. It sounds easy, but it's not an intuitive task. Today, Clay and I will continue our conversation on Robert Hags from's book, Investing,

Starting point is 00:00:27 The Last Liberal Art. We'll explore philosophical mental models such as why it's so important to use the right description when analyzing a business. We'll have a look at the early days of Amazon and how a very prominent investor thought about the Amazon business differently and succeeded in his process. We'll examine the world of literature for ways to supercharge your lessons from reading books. We'll also discuss the importance of narratives in investing in how you can use them to your advantage while also avoiding potentially risky investments. Turning to the world of mathematics, we'll look at Bay's theorem. This is a mathematical concept that you may have never thought about, but probably calculate regularly on an unconscious level. Understanding at a conscious level will help you make better, higher quality of decisions regarding your investing, especially as it relates to valuation.

Starting point is 00:01:13 Then, we'll look at some of the great concepts in the world of decision making. We'll look at System 1 and System 2 thinking and how they can add or subtract from investors' decision-making processes. We'll examine why our intuitive system one shouldn't be relied on very much in investing, and specifically why that is. Lastly, I'll go over my own methods for learning mental models and how I've tried to make them into a habit. I'll also share some of the most impactful mental models I've learned from chatting with some of the wonderful guests on TIP. This episode is a wealth of information if you're on a path of learning and gathering wisdom.

Starting point is 00:01:46 Now, let's get right into this week's episode. celebrating 10 years and more than 150 million downloads. You are listening to the Investors Podcast Network. Since 2014, we 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 hosts, Clay Fink and Kyle Greve. Welcome to the Investors Podcast.

Starting point is 00:02:26 I'm your host Kyle Greve. Today, Clay and I will continue our chat on Robert Heggstrom's book, Investing the Last Liberal Art. So in our chat from a few days ago, we discussed some of the background to why I like these mental models that we've discussed in quite a lot of depth. And then we kind of kicked off the discussion of the very first few concepts that Robert wrote about in his book. So these concepts were physics, biology, sociology, and psychology.

Starting point is 00:02:51 Today, we're going to continue the discussion on mental models and talk about philosophy, literature, mathematics, and decision-making. The section on philosophy was one of the most difficult sections for me to get through, and I think Robert sums up why that may be in the introduction to the chapter. So Robert discusses why philosophy is both the easiest and the hardest areas of knowledge to understand. It's easy because it deals with familiar issues that everybody has to deal with on a daily basis.

Starting point is 00:03:19 But he points out that it's also the hardest because philosophy doesn't come pre-packaged with concrete answers that you can. you would find in areas like physics, mathematics, or biology. And that's exactly why I loved my math classes in high school and wasn't too fond of the philosophy stuff because there was no right answer. The first philosophical example that really stood out to me in regards to philosophy was his points on Benoit Mandelbrot. So he wrote this great book called The Misbehavior of Markets, which is about fractals

Starting point is 00:03:50 and the stock market. In a chat at the Santa Fe Institute, Mandelbrot blurted out that, Failure to explain is failure to describe during a chat on market efficiency. I want to loop this into a wonderful example that Robert covers while trying to explain Amazon in its early days. So Robert looked at how Bears and Bulls described the Amazon business differently. Bears claimed that Amazon should be compared to a business like Barnes & Nobles and Walmart. Amazon at the time sold much of what brick and mortar businesses like Barnes & Noble and Walmart

Starting point is 00:04:22 would sell. So it kind of made sense to make that comparison. So Bears would have said that it was nonsensical for Amazon to be selling at these expensive multiples that it was selling at when comparables were selling at a fraction of its cash flow multiples. But Robert made a really good point that when you looked at the business model of Amazon, it was different. And this kind of goes to that point of failure to describe. So Amazon Bulls would say that comparing to Barnes & Noble and Walmart was just the wrong comparison.

Starting point is 00:04:53 Dell was actually a better comparison. So Robert writes, if you step back and look at Amazon, the company's business operations are more similar to Dell than Walmart. Dell assembles and ships personal computers from various distribution centers located around the country. Orders for computers taken online negate the need for large and costly sales force. Amazon, like Dell, also takes orders online. Also like Dell, Amazon ships products to their customers from one of their distribution centers, bypassing expensive brick and mortar retail locations. The business model allows both companies to operate with negative working capital. They get money from customers before they have to pay suppliers

Starting point is 00:05:33 slash manufacturers, and thus both companies are able to achieve returns on capital well above 100%. So with hindsight, we know that the Bulls had the correct description of Amazon now, and owners of the stock have done tremendously well if they've held through all the ups and downs. But I think the point here about using the proper description is so important because it can help us to determine if we are comparing one business to another business that actually makes sense. If we compare two businesses where, let's say, company A has a P.E. of 35 and company B has a PE of 10, then obviously we're going to conclude that company A is ridiculously overpriced and

Starting point is 00:06:09 probably skip it. But maybe we're not comparing it properly. Maybe it should be compared to company C in a separate industry trading at a P.E. of 50, and therefore, A is actually incredibly undervalued. So this section on philosophy, it really reminds me of the conversation I had with Chris Mayer late last year. We discussed his book titled, How Do You Know? Which Dives into Very Similar Concepts and is very much a philosophy book, which is something I really appreciated about it because I just learned so much from it. One takeaway from that is that

Starting point is 00:06:39 we use these words to describe the world, describe companies and businesses. And it might sound good and it might sound like it makes a lot of sense, but we have to keep a close eye on reality and ensure that it matches with how reality is and how we view the company. One example that comes to mind here is Tesla. When you talk about this Amazon example, I have zero strong opinions on Tesla, but I just can't help but think of this as a more modern day example. You oftentimes hear investors simply compare Tesla's car sales to the number of car sales at these other traditional manufacturers, think GM, Ford, and some of these other players.

Starting point is 00:07:22 And many people come to the logical conclusion that just simply based on the number of cars they're selling, the stock is far overpriced. And they're like, all of their revenue today comes from car sales. And this comparison just doesn't make any sense at all. And all of the Tesla bowls, of course, are viewing the company just through a totally different lens. Again, I don't own shares in Tesla. Haven't looked into the business super closely. But I did do an episode. on the Elon Musk biography back on episode 593. The audience really enjoyed that one. And after you read that book, you understand that.

Starting point is 00:07:54 Tesla is far from a typical car company. So the first item that comes to mind here with regards to Tesla is that they're actively working towards the future in terms of technology, AI, manufacturing, energy. They're really trying to innovate in so many of these different areas. And one way they're benefiting from this innovation is becoming more of a technology. company through these increased inefficiencies in manufacturing. So as Tesla grows, they're able to produce more cars at a lower cost. And then they're continually figuring out ways to add automation to drive down the cost to produce an electric vehicle. And then the biography talks a lot about how

Starting point is 00:08:31 vertical integration is an important part of their business model and producing as many parts in-house that they can. And what they've also been doing is bringing the prices of their cars down as they achieve these higher efficiencies in the manufacturing process, which reminds me of the scale economy shared model that Nick Sleep shared, who was also an early investor in Amazon. And then you could also think about how some of these amazing businesses have a lot of optionality. So in the case of Amazon, AWS produced $90 billion in revenue in 2023, which didn't even exist or wasn't even on investors' radar 25 years ago. So Tesla Bowles, of course, would argue that there's just a ton of optionality. And you'd look at the insurance, you look at robotaxies, monetizing their

Starting point is 00:09:18 self-driving software, monetizing their energy business and so on. So one of the mind-blowing stats I found on Tesla is that they're cumulative miles driven with their full self-driving technology. It's over 800 million miles. And when you look at that chart, it's not just like an exponential chart. It's like a hockey stick growth, whereas like nothing just a few years ago. And that just gives Tesla an immense amount of data that their competitors just won't have. And with machine learning, their technology learns from that, and then it gets exponentially better over time. And I think when viewing some of these disruptive businesses, it's important to remember that a company like Amazon or a company like Tesla has opportunities in front of them

Starting point is 00:10:03 that are very asymmetric. And they deal with these exponential figures. And our minds aren't used to thinking in this manner, you know, where our minds naturally think linearly. So naturally, it doesn't make a lot of sense that Tesla could one day be a $10 trillion company because no company's ever done it. And yeah, our mind just can't really wrap our heads around what sort of asymmetric potentials in front of us. But also, it's very difficult to figure out the probabilities associated with what that's going to look like, which is why the stock is also so volatile. And you don't have to look at these more extreme examples either. Kyle and I own a company called Dino Polska, which is just a grocery store that many people like to compare to other

Starting point is 00:10:45 grocers and just say it's simply expensive based on these various metrics. But no other grocer has the economics that they have or have near as attractive growth. So it's just not really an apple to apples comparison with some of these ways you can compare the businesses. So this mental model can apply to really any industry when you find a company. that behaves much differently than their competitors. I think Dino is such a great example that Clay just kind of brought up. So I actually wrote a piece recently for the TIP Mastermind community where I did actually compare Dino Polska with Geronimo Martins.

Starting point is 00:11:18 Just a little background. Geronimo Martins owns Bedronko, which is one of Dino's primary competitors. So basically I wrote that comparing the two businesses was essentially pointless. And this is for some of the reasons that Clay just pointed out. Dino has very high returns on invested capital and it can reinvest 100% of its profits back into the business. Geronimo Martins is a good company. I'm not denying that. But it's more mature.

Starting point is 00:11:43 It has a return on invested capital that is half of Dino's. And it only reinvest 10% of its profits. So even though these two businesses optically look the same, they sell similar products. They're both brick and mortar. The businesses are just simply different. Therefore, they require different descriptions. So I just wanted to add that. The next philosophical bucket that I want to cover was on narratives.

Starting point is 00:12:06 The reason narratives are so important, especially in investing, is because narratives are such a powerful tool. Stocks can move on a strong narrative alone, but narratives are most powerful when there's statistics that support them. So there was a really good book that I read recently by Azwath de Madaran, and it's called Narrative in Numbers. So Aswath's premise is that stories without numbers are just fair. very tales and numbers without stories to back them up are exercises in financial modeling. So I'm not here to say which is best between being a narrative base or a number based investor, but I can't comment

Starting point is 00:12:39 on my own strategy and what I've learned. So I think a very good story is incredibly important for the development of a good investment thesis. So I know a good narrative is very important to the convergence of the stock price and the intrinsic value of business. So there's a small cap that will remain unnamed, which I own, that required a story to play out for more people to buy into that story as the future unfolded. But I knew that the narrative wasn't enough for me to get interested. I needed data to back that narrative up, to give it a higher weighting and to really get some conviction behind it.

Starting point is 00:13:12 So I researched whether the idea had that data backing it up, and it most certainly did. And so far, the thesis has turned out very, very well for me because the narrative was based up by statistics. So I'm getting, obviously, the narrative is carrying a lot of what's happening with the share price, but also it's all true. So I do believe that narratives are very important, but I also think that statistics or data or whatever you want to call it need to be there in order to back up the story. Narratives and markets are just so interesting. People make up narratives or make up stories because we all just have this intense desire to just really make sense of the world. The reality is we can't really know why things happen exactly the way they do.

Starting point is 00:13:55 We can all make up this narrative or story that sounds good, but we can never really know for sure. I'm reminded of the chapter Morgan Housel wrote in his book, same as ever. I believe it is titled, Best Story Wins. In that chapter, he wrote, The Best Story Wins, not the Best Idea or the Right Idea or the Most Rational Idea. Just whoever tells a story that catches people's attention and gets them to nod their heads is the one who tends to be rewarded. And during my interview with Morgan, he had said, the best product that Elon Musk has ever made is not a Tesla car. It's not a Falcon Rocket. It's Tesla stock. The best product he's ever made is the ticker TSLA, because it's literally one of the most incredible and captivating stories

Starting point is 00:14:38 that anybody ever told. And I think this is something that transcends really to all areas of life because stories play such a key role in so many aspects of how we live. Think about the work that we do, working with our coworkers, think about our relationships, our friends, our family. And think about when you're looking at companies and looking at like a CEO and a management team too. If you don't have managers that can motivate people, then how can you expect a company to be good at what they do? Anyone can just show a group of people,

Starting point is 00:15:11 all the numbers in the world, to try and make sense of something, but numbers don't always resonate with people. It's stories that really resonate with people, and it's what people cling to. But as you mentioned, the numbers are also really important. You know, numbers are based in reality, right? And I think I like the approach you laid out that kind of couples these two together, you know, having a good story, but also having the numbers and the data that can back that up and support the thesis. And I think it's just another reminder to be open to new ideas and new ways of looking at things. These asymmetric opportunities and these asymmetric returns are often found in businesses or in ideas that

Starting point is 00:15:49 look at an industry or look at the world through a different lens that is just generally misunderstood by others. And Hagerstrom points out that the world is really changing as fast as ever, so we really need to be flexible in our thinking and open to these new ideas. Yeah, exactly, Clay. And I just want to go over that Tesla example because I thought it was excellent. And I'm like you. I have no skin in the game and Tesla and I haven't spent too much time on the stock as well. But I do think the story of Tesla's stock is absolutely incredible. And so this kind of actually reminds me of another mental model that George Soros came up with called reflexivity. It's not really a philosophical mental model, but I do think it relates

Starting point is 00:16:27 to narrative. So I just want to talk about it briefly here. Reflexivity is when investors' perception of what's going on can actually influence what's going on and in turn influence their perceptions all over again. So to Morgan Housel's point about the Tesla's stock, it would be interesting to see if Tesla would still be successful today if Tesla remained private. A big reason that nearly everybody in the world has heard of Tesla is from constantly seeing it in the news about the stock, about the run-up and the stock's price. We can't really quantify how much the stock has helped Tesla's operations, but we definitely do know that the story of the stock has improved investors' perceptions in the business. And, you know, let's probably added a few extra Tesla

Starting point is 00:17:08 customers along the way. On that point of reflexivity, it's important for people to understand. When Tesla's stock price goes up, that enables them to be able to do things like issue shares and further capitalize their business and make their business actually stronger. So, like, the better story Elon can tell and the more people he can get to believe that story, no matter how realistic it is, as long as people believe it, that further enhances their ability to raise capital, whether that be from issuing debt, issuing equity or whatnot. And I think that's what a lot of other people tend to miss. They just look at the numbers. They look at the production. They look at how much money they're making. But the story is also a very important aspect to it.

Starting point is 00:17:49 So now I want to move on to the literature portion of the book, which I found very informative, but also the concept that was probably the most malleable. So it's worth noting here that Charlie did not read fiction books, whereas, you know, obviously literature covers both fiction and nonfiction. But Charlie clearly develop his own methods for the books that he wanted to read and how to keep his thinking very, very broad. So one of Charlie's idols, Benjamin Franklin, said in his autobiography, that people should spend less time arguing and more time searching out smart new ways of looking at the world. I think this literature section will help broaden our abilities to learn how to do just that.

Starting point is 00:18:26 So I'm with Charlie Munger in that. I also don't read fiction either, but I resonate with what Hagstrom talked about how developing this mindset of continuous learning and pulling these great ideas from others can honestly feel like a pretty daunting task. But I like how he outlined how he would go about trying to do this. So he explains that the truly big ideas are already out there. They're written down. They're just kind of waiting for us to discover them and make use of them for ourselves. So there's plenty of information everywhere. You got books, podcasts, all these different resources we can pull from and then unlimited information on the internet. And some people like to flex, I think, on the number of books they read. But as someone who doesn't read a book a week,

Starting point is 00:19:11 I think what's probably more important is the ability to retain these key ideas and actually make use of them, which is what Kyle and I will be getting into here. Right. And that leaves, to the first topic that Robert covers, which is this wonderful book by Mortimer J. Adler called How to Read a Book. So you might be thinking it's silly to read a book on how to read, but after reading Hagsrom's book, I went out and actually bought Adler's book and it was really, really good if you want to really, really understand how to deepen your understanding of a book. So Adler's primary concept on reading a book isn't just to read for the sake of information gathering, which I think Clay just alluded to here. We actually want to read to gain a better

Starting point is 00:19:51 understanding of the subject at hand. So Hagsum covers a very simple way to differentiate between when we are reading for information versus when we are reading for understanding. If you are finding that the content is really simple to go through and understand, that usually means that we're just reading for information. So, you know, think about reading popular news like the Wall Street Journal or the New York Times, Financial Times, or Bairns. But when you're reading and have to really stop and think about the thoughts, ideas, and concepts of the book, that is a good signal that you were reading for understanding. And I know this happens to me regularly, well, where I'll be going through a book and I'm like, that seems like a really important concept,

Starting point is 00:20:28 but maybe I didn't grasp it and I need to go back and read it maybe a few times. That means, obviously, I'm really, really trying to deepen my understanding. I know personally that I've focused more and more on deepening my understanding of a topic. And when I read for information, I find it's often, you know, in one ear, out the other. Yes, it's helpful to some degree, but it really is just surface layer content. So this is part of the reason why I think rereading books that have highly impacted me is so powerful. With every time I reread a book, I'm really cementing those core ideas in my mind and

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Starting point is 00:25:16 All right. Back to the show. I think back to Stig and Monish's recent episode. Stig had asked Monish how he takes notes, whether he takes notes on stuff. And I thought it was interesting. Monish essentially said he never is taking notes. He just focuses 100% on the material or the person speaking and just soaks it all in, so to speak and he doesn't let his attention go towards other things. And I think that we all read,

Starting point is 00:25:44 understand, and retain information differently. So we need to find the way that probably works best for us. And I actually spoke with a brain expert here on the show. His name is Jim Quick. And he also wrote a book on how to learn effectively. I picked up a few tips from his book that I wanted to share here that might be helpful to the listeners. So the first lesson I wanted to share here is what he called the forgetting curve. So our natural ability to concentrate wanes anywhere between 10 to 40 minutes of doing a task and you get diminishing returns on your effort after that. So he suggests working in different spurts, say like 30 minutes and then take a five minute break to give your brain a rest and maybe go on a short walk or get up for a bit

Starting point is 00:26:26 and not do something like check your phone or check emails or whatnot. And the second tip is to review that which we want to retain. So maybe you end up taking notes on some of the key points on a chapter or on a podcast or whatnot. And then you go back and review those notes or key takeaways to help increase the retention of what you read, which is something you just alluded to, just going back and rereading it as you're going through it. And I'd imagine that a lot of people, they read a book, they put it down, and then they just go on to the next book and just sort of forget about the previous one they read.

Starting point is 00:26:57 But I think going back and revisiting some of the key points can really be helpful in retaining and making sure you don't forget some of those key lessons. And then the third piece here I wanted to mention was that this was also mentioned by Adler in Hagerum's book is asking yourself the right questions. And, you know, I see your notes here that you're going to be getting to this. But Jim Quick talks about how our brain is a deletion device. And asking ourselves the right questions can help our brain pick up the answers we're looking for because it tells our brain what's important and it helps it sort of filter through

Starting point is 00:27:35 the important information and delete what we deem not as important. Yeah, I love that point about the deletion device. So continuing on with that, why don't we just get into the Adler system here and talk a little bit about the questions that he thinks are so important. Adler basically breaks down just four key questions to keep in mind while you're reading a book to deepen your understanding. So number one is what is the book about as a whole? Number two is what is being said in detail?

Starting point is 00:28:02 Number three, is the book true in whole or in part? And four, what of it? There's a lot more detail that Hags from talks about that it's worth reading in his book. But the main gist here is that when you're reading a work of nonfiction, you should be focused on the key concepts that you're learning from that book and trying to figure out if they are true or if they're more accurate than a different author or what you currently believe. It can also help you think about the author. What's the author trying to tell you in what they're writing? And And is that information usable to yourself? Sometimes it might not be usable at all.

Starting point is 00:28:36 So you can compare what they're saying to other authors who are talking about a similar subject. Maybe they don't agree with each other. And hopefully you can come up with your own insights on which author is more right or wrong on a specific topic. And then you can use that information to make better decisions in the future. There's a really great example of this kind of framework playing out that Charlie Munger wrote about in Poor Charlie's Omnack. Charlie is talking about some of the concepts that he learned from reading Stephen Pinker's book, The Language Instinct.

Starting point is 00:29:04 So one of Charlie's biggest takeaways from that book was that language is embedded deep in the human genome. So this is a concept that Charlie felt Pinker did a really good job of explaining. And in the book, Pinker couldn't understand why one of his contemporaries, Noam Chomsky, just couldn't agree with him on his premise. So here's what Charlie said. Well, the junior professor is clearly right. And Chomsky's hesitation is a little daft. But if a junior professor and I are right, how has geniuses like Chomsky made an obvious

Starting point is 00:29:33 misjudgment? The answer is quite clear to me. Chomsky is passionately ideological. He is an extreme egalitarian, a leftist who happens to be a genius. And he's so smart that he realized that if he concedes this particular Darwinian point, the implication threatens his leftist ideology. So he naturally has his conclusions affected by his ideological bias. And that gets into another lesson in worldly wisdom.

Starting point is 00:29:58 Ideology can screw up the head of Chomsky, imagine what it can do for people like you and me. So I felt like this was just a really good example of Charlie reading a book and then coming to his own conclusions about the general gist of the book. He then compared it to another author writing about similar things and came up with his own conclusions of who seemed to make the most compelling argument and why. So that example points to an idea that Hagerstrom talked about, which is that when we read, we want to try and go in with a clean slate. And rather than looking for confirmation bias or looking for information that aligns

Starting point is 00:30:33 with our current reality, we want to read it and determine whether what they're saying is true or not. Adler also said that you can't understand a book if you refuse to hear what it's saying. I also like the point he made that critical thinking and critical analysis, it's really a fundamental skill to success in investing. So the skill of critical thinking is directly linked to the skill of being a good reader. He writes that good readers are good thinkers. Good thinkers tend to be great readers and in the process learn to be even better thinkers.

Starting point is 00:31:07 So it's no wonder that some of the greatest investors we know today are just avid readers because reading in itself enhances your cognitive ability and it enhances your analytical skills. He also talks about how reading nonfiction and reading fiction is just a totally different experience. For example, reading an annual report or reading some write-up done by an analyst on an individual company versus reading a novel that just tells a story. I think fiction appeals much more to our imagination, much than it does to our intellect. And the content is really highly subjective and it's impossible for an analytical thinker to analyze, which can at times make it more difficult to read in some ways, I think for some people. I think back to when I read Zen and the Art of Motorcycle Maintenance last year,

Starting point is 00:31:59 I got quite frustrated sometimes because I felt like I was just reading all these pages and not getting a lot out of it. But I really wanted to get through the book and I just did my best to hop along for the experience of kind of the story he was telling in it. Yeah, I felt the exact same way on that book as you did, Clay. The last part on literature that I want to discuss here is some fiction examples that Robert mentioned. He talks about some of the great detective stories and references three of the best-known fictional characters here. So I just want to go through these three characters because I do think it's really applicable

Starting point is 00:32:30 to investing. The first one is Auguste Dupin. He has two main points. One, develop a skeptics mindset, don't automatically accept conventional wisdom, and two, conduct a thorough investigation. The next is someone everyone's going to know, which is Sherlock Holmes. So he had four main points here. One, begin an investigation with an objective and unemotional viewpoint. Two, pay attention to the tiniest details. Three, remain open-minded to new, even contrary information. And four, apply a process of logical reasoning to all that you learn. The last fictional character is

Starting point is 00:33:05 Father Brown, who I wasn't really familiar with until I read about him through Robert Hags from. but so his three points are one, become a student of psychology, two, have faith in your intuition, and three, seek alternative explanations and redescriptions. So I enjoyed all these lessons. The themes that really stand out to me between the three of them are skepticism, contrarianism, open-mindedness, and the importance of psychology. I think all of these areas are super important and should be utilized in our investing and analysis and as well as our problem solving. So if we look at one of Charlie's most successful investments in BYD, we knew that he was skeptical about the general stance on China at the time that he bought it.

Starting point is 00:33:45 It was a contrarian move to buy a business in a communist country that very few people understood. The only reason Charlie found the business was because he kept an open mind about China and the opportunities it offered in the future. And I think he was really able to understand the psychological makeup of BYD CEO Wang Chuan Fu before the market did. And I think this is why he and Berkshire were able to earn such good returns on that investment. Yeah, the best investments are those in which most people disagree with you.

Starting point is 00:34:15 And you're correct and your contrarian viewpoint. So in 2016, Apple was purchased by Buffett. It was out of earnings multiple of, say, 10 or 12, and the market viewed it more like a hardware company. Buffett recognized that it was really much more than that. And eventually the market came to agree with him and now he's made 6X's money since 2016. But buying Apple today at a earnings multiple of 30 isn't necessarily a contrarian bet. And being open-minded is required for you to develop that contrarian viewpoint. And oftentimes you have to dig well underneath the surface and get past that surface level

Starting point is 00:34:51 to really get to the essence of a business and truly understand it. I believe Lee Liu helped Charlie Munger with the B-O-I-D investment or maybe they collaborated on that. Lee Lou, he practically turns into an investigative journalist when he finds an investment idea. He likes being a great investor really has a lot of parallels to being an investigative journalist because you're just trying to find out every single thing you can about a company and try and uncover all the information you can and who's running the business, all the industry they operated and just literally everything. And what's also funny is that Apple was an example that was just hiding in

Starting point is 00:35:28 plain sight in 2016. We all had an iPhone. We knew that we probably were going to be using iPhones for at least a number of years ahead. And the market essentially didn't view it as a strong brand, like a Starbucks or a Nike, which we're trading at multiples of 25 or 30. So it points to the fact that a contrarian bet, it doesn't have to be a stock nobody's ever heard of. It just has to be something where your view significantly differs from the consensus. And just because there are dozens of analysts covering it doesn't mean that they can't get it wrong. So turn into mathematics here. I want to start by covering the simple idea of evaluation. So Robert Hagsrom points out here that the formula for evaluating stocks was accidentally created

Starting point is 00:36:11 by ESOP in his tale of the hawk and the nightingale about 2,600 years ago. So the gist of that story was that the hawk caught the character called nightingale. Nightingale basically pleaded for the hawk to release him as he was small and he was trying to say that there's larger game that could be found elsewhere. So the hawk replied, I should indeed have lost my senses if I should let go food ready to my hand for the sake of pursuing birds, not even in sight. So Warren Buffett knew of the story and to complete the evaluation picture, he added three simple questions to answer to find the value of any asset. One, how certain are you that there are indeed birds in the bush? Two, when will they emerge and how many will there be? And three,

Starting point is 00:36:52 what is the risk-free interest rate? Buffett then said, if you can answer these three questions, you will know the maximum value of the bush and the maximum number of birds you now possess that should be offered for it. And of course, don't literally think birds, think dollars. So Hagerstrom's very simple method for this calculation is outlined in the Warren Buffett way.

Starting point is 00:37:11 In the book, he takes the business's owners' earnings and then divides them by the risk-free rate. So owners' earnings simply are net income plus depreciation and amortization, less maintenance cap-x. Hagerm gives an example of this calculation for the Washington Post. So the business in 1973 was at only an $80 million market capitalization. But Buffett said that the business was worth $400 to $500 million. Buffett made a series of adjustments.

Starting point is 00:37:38 He basically understood the newspaper business and he knew that Washington Post in particular would have had earnings that approximate the owner's earnings because depreciation and amortization would essentially equal maintenance cap X. He understood that the Washington Post had latent pricing power and could charge more. He also understood that Washington Post during that 1973 period had depressed operating margins that were likely to increase in the next few years. With all those adjustments, Buffett essentially came to a number of approximately $33 million in owner's earnings.

Starting point is 00:38:12 And then if we divide that by the long-term treasury yield at the time, we get a market cap of $485 million, and that's kind of how he arrived at that figure. Yeah, coming into the world of investing as a beginner, I thought, you know, as a numbers person, it was just all about the numbers, the revenue growth, the earnings growth, the PE ratios. And really, the more I've been a part of this game of investing, the more I've come to appreciate the qualitative factors and how those are probably much more important over the long run. So a member of our TIP Mastermind community, that's an equity analyst, he recently gave a presentation on MasterCard. And he had mentioned at his job that the vast majority of his time is spent on

Starting point is 00:38:53 qualitative factors. So studying the business, studying the industry, studying the management team. But that's not to say that the numbers obviously aren't important. If you get the qualitative factors right, then usually the numbers tend to take care of themselves over the long run. I'm reminded of a blog article that Chris Mayer wrote. He shared this brilliant chart that I wanted to talk through here. It really talks about what dries returns over. certain time frames. So really what the charts getting at is over the next quarter or over the next year, a stock price is primarily driven by the change in the sentiment and the change in the multiple. Those are things that are really difficult, if not impossible, to predict. But as you

Starting point is 00:39:35 extend that time horizon out, it's really, the returns of a stock are really driven by return on incremental invested capital. So what sort of return is the management team getting on their investment? and, you know, that really gets to all these qualitative aspects of, you know, how is a business evolving over time? How is the industry dynamics changing over time? And then over the really long term, he points to the culture and the people within a business. And that is just purely qualitative to a large extent. And it just can't be distilled down to a simple number. And that's not to say that valuation, for example, isn't important. It's just one piece of the analysis. And at the end of the day, Buffett and most other value investors are trying to determine the

Starting point is 00:40:21 intrinsic value, which is the sum of the cash that a business is expected to generate and then discounted back to today. But yeah, the long-term cash generation is really driven by these qualitative factors. And you can't just simplify it down to just numbers. So the next part in the math section that I found really interesting was the concept on Bayesian analysis. So the theorem is quite simple. When we update our initial belief with new information, we get a new and improved belief.

Starting point is 00:40:52 Robert gave a really, really good example of understanding Bayesian analysis. So let's imagine that you and a friend have spent the afternoon playing your favorite board game, and now at the end of the game are chatting about this and that. Something your friend says leads you to make a friendly wager. That with one roll of a die, you will get a six. So straight odds here are one and six, a 16% probability. But then suppose your friend rolls a die again. quickly covers it with her hand and takes a peek. I can tell you this much, she says. It's an even

Starting point is 00:41:21 number. With this new information, your odds now have changed to one in three, a 33% probability. While you consider whether to change your bet, your friend teasingly adds, and it's not a four, now your odds have changed again to one and two, a 50% probability. With this very simple sequence, you have performed a Bayesian analysis. With each new piece of information, it's affected the original probability. So how can we connect this to the world of investing? When I'm looking at a potential investment, often what I'll do is I'll assign probability to a specific outcome. So let's say we have 33% bear case, 33% base case, and 33% bull case.

Starting point is 00:42:00 Maybe the bear case is dependent on the business having some sort of headwind that may or may not happen. Let's say a year goes by and now the bear case is extremely unlikely. For the sake of simplicity, we'll just say it has a zero probability, even though this rarely actually happens in the real world. So now we have a 50% chance of our base case and a 50% chance of the bull case. This is a great asymmetric bet as our downside is now zero and the odds are heavily in our favor. If you understand how this works, I think Bayesian analysis is a very, very handy tool to add to your toolbox. Yeah, I think Bayesian analysis is a really powerful mental model.

Starting point is 00:42:38 It's essentially continuing to update our assumptions and then continuing to update our probabilities. To use an example here, let's say you find a great company as a strong history of success and you expect to continue to do really well. You purchase shares in that business, but the business starts to become disrupted by competitors and their growth comes to a halt. So now things have changed and it probably isn't what you expected. And this can be a difficult situation because when you think about Bayesian probabilities, you might have entered a position thinking that there's a 75% chance

Starting point is 00:43:14 of success or of the bull case. But now that growth has slowed, and now that you have this new information, maybe the odds of success are now down to 50%, for example, and maybe eventually it gets to a point where you think the original thesis is busted and it's time to exit the position. So Bayesian probability is really what's happening here when you decide that you were wrong on the investment and you take your losses. So to use a more recent real-time example, Kyle and I have talked about Dino Polska, and they've actually seen slowing growth in recent quarters, and it's traded down a bit.

Starting point is 00:43:47 And the question I asked myself is, has things changed? In the most recent quarter, they opened 32 stores, and the quarter from a year ago, they opened 54 stores. But when you look back at history, you've seen accelerated store growth. So, you know, we of course need to look into what's happening here. And it might sound like bad news, but they're actually investing in building out four new distribution centers, which are used to supply the stores. And this is actually great news because it essentially means that management is preparing for the

Starting point is 00:44:16 next leg of growth. So investors just need to be patient and let management do what they're going to do, which is execute on that growth and focusing on the long term and not pushing for the short term quarterly headline numbers. So in that case, the Bayesian probabilities really don't change for me. And I still foresee continued growth and a 100% reinvestment rate, high returns on incremental capital going forward. And transitioning here to another part of the mathematics chapter, from time to time I've heard some investors talk about how we're now in a stock pickers market. And the reasoning is that these really big tech companies that we all know about have driven the gains in the stock market for the last 10 to 15 years. And eventually they aren't going to be able to pull

Starting point is 00:45:00 the weight they once did. We've seen a lot of multiple expansion in many of them. And that could lead to, we could call it like a sideways market type environment for the index. And we're all familiar with the bull market, bare market, but sideways markets are when Buffett and some of the other great investors like really shine. So there was a period from 1975 to 1982 where the Dow was flat, so we can call that a sideways market. Just because we may be entering this type of environment it doesn't mean that we should necessarily avoid stocks. During that time period, 18% of stocks doubled over a three-year rolling period. So even though the market overall wasn't really moving, there were still a good number of

Starting point is 00:45:44 stocks that did well. And then if you extend that out over a five-year period, 38% of stocks doubled. And Hagerstrom points out that we can just look at an index or an average and apply that to every stock because there's a significant amount of variation within the market. And I would argue that just about in any market environment, there's always, always going to be opportunities out there. So the final subset of mathematics that I want to cover is regression to the mean. So the Roman, Quintus Horatius Flacchus wrote, many shall be restored that are now fallen,

Starting point is 00:46:16 and many shall fall that are now in honor. So this simple sentence perfectly illustrates regression to the mean. Regression to the mean in my eyes has many similarities to the points on equilibrium that we've discussed a few days ago. Another way to look at it through the lens of investing is that what is hated will once again be loved and what is loved will once again or will eventually be hated. So in that light, it's easy to see the use case for investing. We know that the stock market is mostly efficient and that stock prices usually track

Starting point is 00:46:47 intrinsic value most of the time. This is regression to the mean. So Francois Rochon, who Clay recently interviewed, had a wonderful illustration of how regression to the mean can work. So he took the sum of the earnings per share and the dividend yield for all the businesses in his portfolio each year. He calls this owner's earnings. It's worth noting that this isn't the same as Warren Buffett's owner's earnings. So he has this very simple chart that he put out in his annual report.

Starting point is 00:47:15 And he basically annualizes the numbers since the inception of his fund in 1996. So when you look at the owner's earnings, they're 12.9%. And can you guess what the market returns of his portfolio? Yes, it's 12.9%. It's pretty impressive. So he ran the numbers as well for the SP 500 and they came out as owner's earnings of 8.6% and a return of 9.6%. Now, how does this relate to regression in the mean? Rochon really understands that over the short term, value and price can become disconnected. So in the chart that he has on his annual, he shows the difference between changes in owners earnings and the difference in market prices. And since 1996, there was only one year. where there was zero difference between the increase in the intrinsic value of his business and the increase in the market price. So every other year, there were these swings that were widely. You know, prices were maybe going up 20, 30 percent up or down, whereas the intrinsic

Starting point is 00:48:11 value maybe was going up in that 12-ish percent range. So the point in the long run here is that market will regress upwards or downwards towards the increase or decrease in intrinsic value of your portfolio. This ties in really well with Howard Marks' book, Mastering the Market Cycle. So when you look at a lot of great businesses, their intrinsic value just tends to increase over time as their profits increase you after year. So what you tend to see is the stock price will typically oscillate around that intrinsic value.

Starting point is 00:48:43 And really what causes that is human psychology and human behavior. Howard had said on our show that humans tend to take things too far. So stock prices rarely stop at the intrinsic value when it's going up. It tends to exceed it. And eventually it turns the other way and oscillates back below the intrinsic value. And this is something Kyle and I also covered in our previous chat. So what's interesting, though, for what you pointed out for the Francois Rochon example, is that almost always, essentially always over a long enough time period, the intrinsic value is the dominating factor in the long run.

Starting point is 00:49:20 So one of the statistics from Marx's book that is just amazing to me is that it's pretty common knowledge that the return of the stock market tends to be in that 8 to 12% range, depending on what time period you look at. But the market hardly ever delivers returns within that range. And that's human psychology and human behavior at play. So from 1970 through 2016, so 47 year time period, there were only three years where the market returned between 8 and 12%, which just seems bonkers. It's because we're all so used to that 8 to 12% return.

Starting point is 00:50:00 You know, it tends to be what people expect. But it's from a year to year basis, it's almost never a reality. So we're told that the market's going to return around 10%, but it hardly ever actually delivers that. And I think it really highlights the impact of cycles and how the average returns are much different from the returns we tend to see year to year. And I also think that once you understand this, you'll likely be much more eager to invest when stocks are down as opposed to when they're up, because you know eventually the intrinsic

Starting point is 00:50:31 value is going to tend to dominate over the long run. And things always seem to naturally swing back. That pendulum seems to come back the other way eventually. Let's take a quick break and hear from today's sponsors. No, it's not your imagination. Risk and regulation are ramping up, and customers now expect proof of security just to do business. That's why VANTA is a game changer. Vanta automates your compliance process and brings compliance, risk, and customer trust together

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Starting point is 00:53:44 current distribution rate as of 1231, 2025. Carefully consider the investment material before investing, including objectives, risks, charges, and expenses. This and other information can be found in the income fund fund's prospectus at fundrise.com slash income. This is a paid advertisement. All right, back to the show. So I want to start the part on decision making here by using one of the three questions from the cognitive reflection test that Robert cites in the introduction to his chapter. So if a bat and a ball costs $1.10, then how much does the ball cost? There's a very good chance that your answer is going to be wrong here.

Starting point is 00:54:22 But even the brightest minds at MIT, Harvard, and Princeton got this answer right only 50% of the time, so don't beat yourself up about it. The first time that I was exposed to this question, I can admit that I got the answer completely wrong as well. The first time I read it was in Daniel Connman's thinking fast and slow. And so the primary point of that book was that we have two systems of thinking. Conneman calls them System 1 and System 2. When we were thinking about a question like the bat and ball problem, we default to our automatic

Starting point is 00:54:49 system 1 to find out the answer. But there's a problem with that. System 1 often does not provide the right answer. So it's worth noting that system one is very powerful and necessary. It's a more intuitive system, but intuition very often does not work out. And intuition works very well in very specific conditions. So system one, for instance, if we look back at history, if we see an animal that's going to try and eat us, it tells us to run away.

Starting point is 00:55:14 Obviously, that was very, very important. But these days, obviously, that intuition is not very useful and obviously gets us into a lot of trouble with certain things. Getting back to that bat and ball problem, if we want the correct answer, we should probably actually rely on our more effortful system two. So this system is where slow, deliberate, deep thought occurs, and we can at time come up with much better answers relying on system two versus relying on system one. So one part of the book that I highlighted was Conneman's conclusion on intuitive thinking. Intuitive skill exists mostly in people who operate in simple, predictable environments and that

Starting point is 00:55:50 people in more complex environments are much less likely to develop this skill. That example of the bat and ball is just so funny because it really just helps you realize in real time system one and system two thinking happening within you. And we can be so quick to jump to incorrect conclusions and feel married to that conclusion in a way. And I think back again to when we're in school, we're sort of conditioned to answer questions quickly and always just be working against the clock on a problem, which essentially encourages system one thinking and getting to an answer quickly, rather than taking a step back and ensuring that we're thinking through it correctly. So that point on intuitive skill is really interesting too. Connman accepted the idea that

Starting point is 00:56:36 army officers, firefighters, physicians and nurses, these are all fields where you see many of the same things repeated over and over again. So people in these fields can naturally default to system one thinking. But investing, unfortunately, is much, much more complex. than a lot of people are led to believe. An intuitive skill is much less common in this arena as a result. So we need to develop the skill of really thinking through things closely and not coming to conclusions too quickly through System 1 thinking. And it's pretty clear that all the successful investors I've chatted with,

Starting point is 00:57:13 they are all extremely deep thinkers that study businesses very, very thoroughly. So success is no accident when it comes to investing. One way to ensure that we're using more system two thinking is to do something like utilize a checklist that requires you to understand a business really well before making decisions on it. It's easy to act on a stock when you see high revenue growth and optically low valuation, multiple. But oftentimes these surface level ideas that require system one thinking aren't really going to move the needle for you.

Starting point is 00:57:49 You might get lucky and it ends up working out. but it's a system two thinking that's going to drive a lot of your performance, I think. Lawrence Cunningham had a quote in his book, Quality Investing that I think sums up the power of checklists pretty well. He wrote a good checklist should enumerate all the desired attributes for an investment, and ideally the steps required for full due diligence. It should also incorporate lessons learned from previous mistakes and be regularly updated accordingly. Yeah, so, Clay, your point here, about how school encourages system one thinking really resonated with me.

Starting point is 00:58:25 I always found in school that I would finish a test and earlier than other people, and I would always be proud that I answered every single one of the questions. But unfortunately, I wouldn't always get all the answers right. So if I'd been given more time and had that ability to have less pressure and really come up with the right answers and use system more of my system two, then perhaps I would have gotten the correct answer and gotten better grades. But I digress. So the next thing I want to discuss is hedgehogs and foxes.

Starting point is 00:58:51 So Hagerm points out that 2,600 years ago, a poet named Archilicus, who I probably am butchering there, wrote that the fox knows many tricks, the hedgehog, only one. So you may be thinking, what does this have to do with decision making? So that was kind of a question that the great Philip Tetlock, author of Super Forecasters, tried to address. In one of his studies on expert political judgment, he separated forecasters into two groups. So one was hedgehogs and one was foxes. Both groups didn't perform well, but that wasn't the interesting takeaway. The interesting takeaways was what he found that there was just more success in forecasting in the Fox category compared to the hedgehog category.

Starting point is 00:59:32 So the next thing he sought out was what was the reasoning for this? Why did the hedgehogs underperform? He came up with two primary conclusions. So one was that hedgehogs tended to fall in love with their pet theories. And number two was that hedgehogs were slower to change their mind. So in contrast, Tetlock concluded that the foxes had three superior advantages. Number one, their initial estimates are closer to base rates. Number two, they are more apt to adapt their decisions as quickly as new information is released.

Starting point is 01:00:03 And finally, number three, they adapt to the pull of just confirming evidence. So I think it's quite obvious in the world of investing that being a fox is probably going to be a more superior choice than being a hedgehog. And the points above are all highly relevant to investing, especially that second and third point. As investors, it's obviously super important for us to be able to change our minds based on new information that we have at our disposal. If you can't change your mind on the stock where the thesis is completely changed, then you're likely going to hold really poor investments for long periods of time.

Starting point is 01:00:35 So I just wanted to make one kind of argument here about the hedgehog characteristics. So there's one that I actually think is somewhat beneficial to being a long-term investor. That is the point that hedgehogs are slower to change their mind. So I actually think for me, being slower to change my mind has become more and more of a benefit than a detractor. So let's say I buy into a stock that I think is going to perform very well over a multi-year time period. During that multi-year time period that I'm holding the stock, there's going to be so many small events and noise and things that just don't really matter for that business that are going to affect the price of. it's going to go up, it's going to go down. And if I'm really quick and I see, okay, the price has gone down, I'm really quickly to sell it because I'm scared, well, that's not really going to help

Starting point is 01:01:24 me in the long term. So I think slowing down and really thinking about the decisions and really thinking about where you might be right or wrong is actually really smart. So it's kind of this push and pull. Sometimes you have to be quick. Sometimes you have to be slow. I love the fox and hedgehog comparison because it reminds me of John Huber in the way he invests. I thought it was so interesting that John is really looking for three types of investments. So you have your typical compounders or higher growth businesses, think like a copart which he owns. The second is blue chips that are out of favor. So Apple in 2016, Amazon in recent years. And then the third is bargains or special situations. So this is very much like a Fox type approach where he's,

Starting point is 01:02:10 He's not just looking for one type of company. And a lot of investors, I think, just look for one type of company. And this is really a hedgehog approach. And I think John is really flexible to acting opportunities that he thinks are best within the market and weighing the opportunity costs with what the market's giving him. So quality investing is what Kyle and I have talked a lot about on the show. It's gained a lot of steam over the past Decatur show. But John points out that Buffett, for example, he really,

Starting point is 01:02:40 rarely pays over 15 times earnings for a public company. Oftentimes, he pays 10 times earnings or less. I think John recognizes the power of owning great businesses, but is also cognizant to the fact that investors are starting to realize this as well, and he needs to find companies that the market doesn't yet find to be great yet. So to your point, foxes are better at recognizing really base rates. And while it might work out to pay, say, 30 or 40 times earnings for a great company, he knows the base rates of success might not be as good relative to the other opportunities in the market. So another thing I picked up from John that I think really applies well here to decision making is that he spends a lot of time writing and journaling. And I think that

Starting point is 01:03:27 really helps him clarify his thought process. And then he also spends a lot of time just giving his mind space to think. So he might write or read something and then go for a long run. And his mind really has time to process what it is he was writing or reading about. And it's clear that that has been a really powerful tool to help him clarify his thinking and really give him his mind space to think about these difficult problems. And I like how Hagstrom sort of summarized the difference between the approach of the hedgehog and the fox. He writes, hedgehogs start with one big idea and flow through, no matter the logical implications of doing so.

Starting point is 01:04:06 In foxes, they stitch together a collection of big ideas. They see and understand the analogies and then create an aggregate hypothesis from there. So foxes are just a perfect representation of using these mental models and then implementing them within our toolkit. So I think that most people would associate high levels of intelligence with high levels of rationality. But Keith Stanovich in his book, What Intelligence Tests Missed, the Psychology of the Rational Thought, would tell you that the two are definitely not correlated.

Starting point is 01:04:39 So in the book, he created the term dysrationalia, which is the inability to think and behave rationally despite having high intelligence. So reading about dysrationalia made me think of a really good book I recently finished called When Genius Failed by Roger Lowenstein. So the book talks about long-term capital management's rise and fall. The business was chock full of talent and brains, and unfortunately, that wasn't enough to produce long-term success. So Buffett summed this up incredibly well in 1998.

Starting point is 01:05:09 The whole long-term capital management, I hope most of you are familiar with it, the whole story is really fascinating because if you take John Merriweather, Eric Rosenfeld, Larry Hillenbrand, Greg Hawkins, Victor Heghani, and the two Nobel Prize winners, Merton Scholes, if you take the 16 of them, they probably have as high of an IQ as any other 16 people, working together in one business in the country, including Microsoft or whatever business that you want to name. So an incredible amount of intellect in that room. Now, you combine that with the fact that 16 had had extensive experience in the field they were operating in. They weren't a bunch of guys who made their money, you know, selling men's clothing and then all of a sudden went into the securities

Starting point is 01:05:49 business. They had in aggregate, the 16, probably 350 or 400 years of experience doing exactly what they were doing. And then you throw in the third factor that most of them had virtually all of their substantial net worth in the business. So they had their own money up, hundreds and hundreds of millions of dollars of their own money up, super high intellect, working in a field they knew, and essentially they went broke. That, to me, is absolutely fascinating. Buffett further went on to say, but to make money they didn't have and didn't need, they risked what they did have and did need. So Stanovich said here that there are two primary causes of dysrationalia. One, process problem, two, content problem.

Starting point is 01:06:30 I'm going to let Clay cover that in a little more detail. Yeah, so piggybacking on what Stanovich was saying there, he believes that humans generally process information poorly. So some people are slow and thoughtful in their decision making, think the system's two thinking. And then other people tend to really use very little effort and just make the snap decision. So that's really system one thinking.

Starting point is 01:06:54 And most people tend to default to using lower levels of concentration, simply just to preserve energy. Or in other words, most people are just wired to be lazy thinkers. And they take the easy way out when solving problems. And as a result, their solutions aren't really the best. So they use system one instead of system two thinking.

Starting point is 01:07:13 I think one way to try and apply more system two thinking is just to ask yourself the hard questions. When you ask yourself the right and the hard questions, your mind starts to look for that answer. And it can be difficult to dig past that initial reaction we have, but it's what you really need to try and do. And then the second issue was not having the right content to make proper decisions. Stanovich refers to the gap between what you currently know and what you need to know. He refers to this as the mindware gap, which can really be obtained through the same. study of broad subject matters, which also is what he refers to as the metacurriculum. And this is why value investors always emphasize continuous learning. We should always be

Starting point is 01:08:00 looking for new knowledge and new tools to add. And Hagstrom explains that even when we're able to find inefficiencies in the market, we still shouldn't let that stop us from acquiring new building blocks of knowledge to add to our repertoire. So he uses a number of examples to explain why this is. But the one I liked the most is that it's like a company continuing to spend on research and development. So in the short term, it might look good to not spend on research and development because it boosts the short term earnings and then potentially boosts the stock price. But over the long term, there's so much to be gained from research and development. You know, it can bring new innovations, ensure that companies staying ahead of its competitors,

Starting point is 01:08:43 ahead of the curve. And it also gives them an embedded asymmetry of discovering something that they would have never discovered otherwise. And it allows them to just gives them a ton of opportunity for growth over the long run. So the same mental model we can really apply to ourselves, you know, continuing to learn new things and be on the lookout for, you know, ways in which we can improve as investors. That's most of the mental models from the book. But there's a couple more things that I wanted to chat about. So in the chat that we, that Clay and I had a couple days ago, I covered that I wanted to go over a couple ways that have helped me with thinking in mental models and more importantly making that really into a habit. I've kind of been running this system

Starting point is 01:09:25 now since the last quarter of 2023 and I'd like to share it with you about how I'm trying to make thinking and mental models more of a habit. The first thing I'm really trying to do is actively trying to learn more about these mental models and many of them that we've gone over today. So I think that you can pick and choose kind of how much time you want to spend on a new mental model. Maybe it's a week at a time. Maybe it's a month at a time or whatever that you think is both reasonable and doable. So I've been using a lot of different resources to learn just mental models.

Starting point is 01:09:54 So obviously there's the book that we went over today investing, The Last Liberal Art by Robert Heggstrom. There's a wonderful three-part series called The Great Mental Models by Farnham Street. There's Farnham Street blog, which it does a wonderful job of going over tons of the mental models in the book and giving you different perspectives. There's Poor Charlie's Alman by Peter Kaufman. There's two books by Michael J. Mobison that I really like, Think Twice and More Than You Know, awesome books.

Starting point is 01:10:21 And then, you know, once you know some of these mental models, you can literally just type it into Google or YouTube and you can get down some very, very deep rabbit holes there as well. So there really is, it's easy. You can, if you wanted to, you could just use Google and learn all these mental models for and pay zero dollars. but the books I find sometimes just give you a little bit of an edge and lead you in the right direction. The trick here, though, if you do decide to go down a rabbit hole, it's not really necessary.

Starting point is 01:10:47 You don't need to spend 40 hours trying to understand some obscure physics concept. I mean, like we've kind of been discussing here, you just need to know the big idea behind some of the most important ideas from different subjects. So once you start hammering away at those mental models and start learning them and understanding them a lot better. The next step and probably the more important step is that you want to make using them as part of your thinking into a habit. So to do this, I've kind of figured out two ways. Number one is to journal about it. And then number two is just actively thinking about it. So you don't have to do either of them. You can do one or the other. I think it's pretty hard

Starting point is 01:11:27 to not get better at this. If you don't do one of them, I think active thinking is probably going to be the best. So if you, but let's say you journal. If you journal, you know, you can make a prompt every time you journal or maybe once a week or once a every few days, whatever, and just connect it to mental models. So what I would do, I would just think about some mental model that was learning. Then I would connect it, whether that's to a business I own in my portfolio, a business maybe that I'm researching, but then also just to my entire life, I would, I might be thinking about, let's just say I'm thinking about the mental model of inversion, which we didn't go over, which is probably my favorite one. But I might be thinking about

Starting point is 01:12:01 inversion and friendship, for instance, nothing literally to do with investing. So basically, what I would do is I would just invert what being a good friend is. Try to find out how to be a crappy friend and then just try to avoid that all costs. And I would maybe write down what some of those things are. Some of them might be really obvious, but it also kind of gets your mind thinking about maybe some of the less obvious things. You know, if you don't want a journal, because I know a lot of people don't,

Starting point is 01:12:23 that's totally cool. You could also just, you know, set a timer for whatever, five minutes, 10 minutes, however much time you want and just think about the exact same thing, you know, make the prompt about whatever mental model you're using, then just try to connect it into whatever's happening in your life, whether it's friends, family, business, relationships, whatever. So I think if you do this for a long time, you'll really start thinking about things like your life, family, works, stocks in a different way.

Starting point is 01:12:52 And you're going to actively start trying to connect your life to the mental models that you are using and that you're obviously starting to understand better and better. You'll notice links between the models. you'll try and find ones that work, but most don't and see that you're finding the right one or if you're trying to stick a square peg into a round hole. So like for instance, you know, let's say you know 20 different mental models and you're thinking about a problem. You might run through those mental models and you might be like, okay, well, you know,

Starting point is 01:13:19 does critical mass, does that have literally any bearing to the problem I'm trying to solve? And most of the time you're going to say no and you just move on to the next one. And I think this is where Charlie was so good. He knew so many of them. He could just run down through them. instantly know which ones he used. So this isn't an easy process. I'm not going to lie and say that you can snap your fingers in tomorrow. You're going to have the same abilities that Charlie Munger has. But I do think that the more and more you practice it, you're going to really, really

Starting point is 01:13:45 cement your ability to think this way. Like I was saying about Munger, I think he's a genius. I just think he thinks in a different way. I don't think I'll ever get to his point. But, you know, if I can get to 25% of how good he was, I think that I will be wildly, wildly more successful. And so my whole point here is basically in just trying to systematize thinking in mental models. And I think as you do it more and more, like for me, I'm noticing I'll think about a problem and I'll automatically start thinking about mental models without even having to prompt myself to do it. So my hope is that, you know, doing this over many, many decades is just going to continue refining

Starting point is 01:14:21 that process. Yeah. And Munger had all these models just in his head supposedly. So he's just thought about this so, so much. and all that he's read and all the work he's done and just thinking through problems wisely. So you've been sort of doing this exercise for six months or so. I'm curious what some of your favorite mental models have been that you've reviewed over that time and kind of honed in on.

Starting point is 01:14:48 Yeah, absolutely. So there's been a lot. So I'll just go over a couple of the more recent ones. So I've actually learned some really good ones from the guests that I've had on the show pretty recently, actually. So through researching and chatting with Annie Duke, on TIP 623, I learned a lot about what she calls her kill criteria. So this is basically just a simple way to create criteria that will kill an idea.

Starting point is 01:15:11 So she covers a ton of different examples in her book, Quit. But I really enjoyed talking to her because we got to really specifically look at these kill criteria through the lens of investing. For me personally, I know that I've held on to a couple of investments for just too long. And I think if I had this mental model of kill criteria, I could have, you know, you used it to kill my hypothesis probably sooner and probably would have avoided keeping these losers in my portfolio for as long as I ended up doing it. So just giving an example of what a kill criteria might be, we obviously talked a lot about Odenopulska. I'm just going to continue on with

Starting point is 01:15:46 that theme. So let's say Dino Polska's store count started decreasing. That to me is pretty much a kill criteria. I mean, maybe there's some very, very good reason for that, but my hypothesis is built on growth and the business's ability to continue reinvesting. So if they're closing stores, well, that probably isn't a very good thing. So that might be a kill criteria, in which case it would signal maybe an exit from the business and then finding another opportunity to invest in. Another set of mental models I learned was speaking from Vitale Katzenelson on TIP-617. So Vitale and I talked very extensively about some of the stoic tools that he's used to help just navigate a really complicated world. So we discussed four tools, but there was one, I think,

Starting point is 01:16:34 that had the biggest impact. And that was the dichotomy of control. So basically the dichotomy control, it's super simple. It's just there's certain things we can control. There's certain things we can control. I think that if you view the world in that lens, it really helps you become much, much more resilient, which is what a lot of stoicism is about. So dichotomy of control is incredibly valuable in investing. You know, obviously in investing, there's tons and tons and tons of things that we have no control over. And but we do also have control over a number of things.

Starting point is 01:17:06 So for instance, I have no control over what the market thinks about the stocks in my portfolio. You know, one day they might love them. One day they might hate them. Most of the time, probably going to be completely neutral on them. Knowing that, why should I be upset when the prices fluctuate on these things that I have no control over. The answer is I shouldn't, but I can get upset about what I can control. What I can control is things like what businesses are in my portfolio. I'm looking for businesses that I have a high certainty are going to continue to increase their intrinsic value, and I can

Starting point is 01:17:40 buy them at either a reasonable price or super cheap. So that is what I can control. That's kind of what I focus on. So if a business is no longer increasing its intrinsic value or maybe the intrinsic value is decreasing, again, I have the ability to remove that stock from my portfolio and replace it with a better option. So I think that dichotomy of control is super important and understanding just in the market, there are so many variables that we have no control over. And obviously, you just kind of have to live with that. And that's kind of a feature of the market and focus on what you can control and focus on just trying to find really good investments that fit your own investing philosophy and your own investing criteria. Yeah, so I'm glad you

Starting point is 01:18:22 mentioned just like you've been pulling these mental models from the guests we've had on the show because it sort of makes you realize, you know, finding these mental models and implementing them into our lives. Like it's something us and our listeners just naturally do. You know, we're bringing on many great investors. We're talking about many of these great books that authors have put out. And yeah, I mean, all of us are sort of in this journey together of finding all these mental models, finding the ones that, you know, make the most sense to us to kind of add to our toolkit and then going from there. And the one you mentioned with Vitali is super powerful of there's so much that's just outside of our control. And you know, you can really get hung up in these things that you have no

Starting point is 01:19:02 control over because, you know, life inherently is just really, really hard. And, you know, once you kind of develop that stoic mindset and develop, hey, here's the things I can control. Did I make a mistake that I can learn from? And, yeah, just trying to differentiate of the things we can't control, like can you really do anything about it? I think. Sometimes. it's just like, it's just the role of luck playing in and, you know, there's not much we can learn, but a lot of times you can learn something when you get that feedback externally. So, yeah, 100% in terms of managing a portfolio, sticking to who your circle of competence, I think is really important. And sticking with businesses you know well, a lot can go wrong when you get

Starting point is 01:19:42 into stuff that you don't know well. So that's all we have for you on today's episode on investing the last liberal art. Thank you very much for tuning in. Thank you for listening to TIP. Make sure to follow We Study Billionaires on your favorite podcast app and never miss out on episodes. To access our show notes, transcripts or courses, go to theinvestorspodcast.com. This show is for entertainment purposes only, before making any decision consult a professional. This show is copyrighted by the Investors Podcast Network. Written permission must be granted before syndication or rebroadcasting.

We Study Billionaires - The Investor’s Podcast Network - TIP641: Improve Decision Making with Mental Models w/ Clay Finck & Kyle Grieve

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