We Study Billionaires - The Investor’s Podcast Network - TIP146: Superforecasting - The Art and Science of Predicting (Business Podcast)

Starting point is 00:00:00 You're listening to TIP. So a few months ago, we had a huge name in finance on our show. And the gentleman's name is Ed Thorpe. And Ed Thorpe, his personal net worth is around $900 million. And during our discussion, I was talking to Ed Thorpe about certain ideas about what might happen in the future, particularly about central banking. And I asked Ed a really difficult question. And I was kind of expecting him to tell me, yeah, I think there's a high. probability that that might be the case with respect to central banks.

Starting point is 00:00:34 And what I got back from him was kind of an interesting response. Ed basically said to me, I have no idea. And he responded so quickly without any hesitation that it just shocked me. And during that interview, Ed said, you know, you really got to read this book called Super Forecasting by Philip Tetlock and Dan Gardner. And because of that interview and because of that moment that I experienced with Ed and how he responded to this question about forecasting. And then he recommended this book for us to read.

Starting point is 00:01:07 That's why we decided to do this episode today on Super Forecasting. And everyone really forecast, whether about it's the weather, beating the morning traffic, or it's the financial markets. And while forecasting might appear to be a game, it's in fact very real. And the stakes are high and substantial. As a society, it's important that we hone the skills of forecasting because, because countries starts embracing evidence-based policies, which basically means that we are trying to forecast.

Starting point is 00:01:36 And it's also true on the personal level because the ability to forecast is the difference between success and failure in life and business. So in this episode, we're investigating why some people forecast better than others and we'll teach you the techniques to think rational about your own predictions. All right, guys. So if you're ready, we're ready. Let's go ahead and do this.

Starting point is 00:01:56 You are listening to The Investors Podcast, where we study the financial markets and read the books that influence self-made billionaires the most. We keep you informed and prepared for the unexpected. Okay, so let's get this episode going here. Stig, as we said in the intro, we're talking about the book, Super Forecasting by Philip Tetlock and Dan Gardner. And I really like this book. I'm just going to throw that out there. I thoroughly enjoyed some of the discussion here because when you're talking about stock investing or any type of investing, it all comes down to what you kind of expect the future to look like and what you're kind of estimating those future cash flows to be and you're discounting those back to which you think your return might be. And so this was such a relevant book for us and the writing and it was really good.

Starting point is 00:02:57 It was easy to understand. It wasn't a difficult read. But in general, I really liked it. I'm just trying to capture your thoughts here before we start plowing through this chapter by chapter. Yeah, you know, I think it's very important for people to realize that we forecast all the time. And as stock investors, we automatically think about stock investing. But you're also forecasting whenever you're leaving in the morning, can I beat the traffic? I mean, it happens all the time.

Starting point is 00:03:20 And how do you come up with these conclusions and how do you forecast the best? And that was really the interesting thing about this book. All right. So let's just go ahead and jump into this. So chapter one is titled, an optimistic, skeptic. And so this chapter was pretty generic to start off the book. And what it's really getting at is when we think about forecasting into the future, a lot of people might have the cop-out statement that you can't predict the future as impossible. And that's not true either.

Starting point is 00:03:46 So the example that Tetlock and Gardner are using their book is, hey, if you look at the forecast of what the weather might be tomorrow, there's a pretty high probability that they're going to be really close to what the truth is with respect to the temperature, whether it's going to be sunny, rainy. But where it gets more difficult is when you start stepping to a week into the future and it becomes more fuzzy. And what they're really trying to get at is this idea of an array of possibilities. And this is something that we've talked about on the show numerous times in the past. But when you think about where you're at right now in time, you know what's happening around you. But if you had the forecast what's going to happen in the next minute, you have a pretty

Starting point is 00:04:26 good idea of what will happen in the next minute. If you had the forecast in an hour, the possibilities of what could happen start opening up more. Your left and right limit of potential starts opening up more. And when you push that out further and further call it a year, that's when it starts getting to be very difficult depending on what you're talking about. And so that's what they're really talking about in the first chapter is opening up this idea that forecasting can be done. It's just the difficulty and the probability of that changes as you extend that timeline into the future. And the author has a really interesting discussion about how we don't usually check up on forecasters track record. So whenever you hear people in the news

Starting point is 00:05:06 talking about what they think will happen, you don't talk about if the forecast had been accurate in the past. And he relates that to a sports team. Like, would you ever acquire a player if his stats wasn't good if he couldn't prove that he's actually capable of carrying up that task? Something else that I want to highlight is the whole name of the book super forecasting is this idea that both of the authors conducted this experiment and they were working with the government on this idea. Are there people out there that are better at conducting forecasts of the future than the average person? And what they found out is that this is a true statement, that there are a super forecasting group of people in the world that are

Starting point is 00:05:50 good at this. They statistically prove that they can outperform the typical person, making predictive analysis. And a perfect example would be the whole North Korea thing that's happening in the world right now in the end of the first quarter of 2017. There's a lot of talk about is the United States or China or anybody going to go into North Korea and do something. So that would be an example of an event that they would have super forecasters versus regular forecasters trying to predict whether that's actually going to happen or not.

Starting point is 00:06:22 And so through their research, they had proven that super forecasters exist. And so in this book, what they're doing is they're outlining and trying to understand what separates those people from the normal forecasters. Why are they able to make better predictions than the typical person? And so he goes chapter by chapter talking about some of these dynamics on how they're able to do it better than the typical person. And the interesting thing is that the intelligence agencies actually are really interested in this because what they prove in this book is that the best forecasters are a lot more

Starting point is 00:06:52 efficient than the agencies, which shouldn't make any kind of sense because they have thousands of thousands highly skilled people trying to predict or forecast, if you will, what's going to happen in the future. So why is it that a few handful of people are doing so much better? What can we learn from them that can be implemented into our intelligence services? So that's also one of the reasons why he'd been writing this book. And a really neat highlight is that some of these people that are considered super forecasters are like one gentleman was a farmer out the middle of the U.S. who had no government ties and like some of the backgrounds of these people are just quite phenomenal. And you're wondering how are they different and how are they able to do this without the

Starting point is 00:07:35 background of maybe a trained professional that has a niche in a specific area. And so the authors talk about why that exists. So we're going to get into some of that. So let's jump in the chapter two. and the title of this chapter is illusions of knowledge. And the premise of this chapter is pretty simple. What he's saying is that a person who's very knowledgeable in a specific area, they sometimes, not all the times, but sometimes have a bias towards what it is that they actually know versus don't know.

Starting point is 00:08:05 And he uses an example of a doctor who's providing a recommendation for medicine that a person should take. And the person who's receiving this diagnosis and the prescription that's associated with that, they just take it at face value because the person has the assumption. They're just like, well, this person has to be right. They're a doctor. They have to be 100% right is the mindset of a lot of people. But what the authors talk about is that is actually pretty far from the truth. In fact, a lot of doctors misdiagnosed different things and they get it wrong.

Starting point is 00:08:38 and that this culture in medicine is that it's not, or at least it wasn't in the past. I think they've made a lot of changes more recently in the last 10 years, but some of this still persists in the culture where people don't question, are you making the right decision? Where are we making a mistake? What is wrong about my analysis when I'm thinking through this? And he uses the medical community to kind of highlight this illusion of knowledge is the way that they describe it in the book. Basically, we're talking about confirmation bias,

Starting point is 00:09:11 a type of bias that we had talked about many times before, where you always look to find a reason why you're right and other people are wrong. I definitely know that for myself. Whenever I read something in the news about the stock market being correctly priced or even undervalue sometimes, immediately I don't want to read it. And if I do read it, I'm like, I'm having this mindset of now I'm really going to try to disprove this guy. And that's even before I started reading the article when he's saying that if you have an open mindset,

Starting point is 00:09:41 something that's really carcouristic from a super podcaster and not saying, I know what the truth is, but rather I'm seeking the truth, then you'll be a lot more successful. So I try to learn from that. And for the person who's hearing this and thinking, well, how can I prevent that from happening? I think it's really simple. So in order for a person to prevent confirmation bias from occurring, let's just use the stock market for example. My personal opinion is that it's highly priced and that there's a lot of risk in owning it right now. If that's truly my opinion, the articles I should be reading are articles that support the stock market going higher and people who think that it basically has more to run and their reasonings for why it has more to run.

Starting point is 00:10:26 I should be reading all those kind of articles to counter my opinion and to basically remove that bias. Now, am I good at that? No, I'm not good at that. And I think part of the first step is admitting that. But I think that that is a really, really important highlight and something that you take away from this book that a lot of people definitely don't do because the majority of people, they have an opinion. And then they go and search for that on Google and what are they going to find? They're going to find other people that have similar opinions. And then they're going to read that and it's going to solidify that opinion.

Starting point is 00:10:59 And they're going to even get more hardened into that opinion instead of trying to troubleshoot it in. find maybe the array of potential outcomes that support the direction of forecast going a different direction than what they expect. Let's take a quick break and hear from today's sponsors. All right. I want you guys to imagine spending three days in Oslo at the height of the summer. You've got long days of daylight, incredible food, floating saunas on the Oslo Fjord, and every conversation you have is with people who are actually shaping the future.

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Starting point is 00:15:15 That's Shopify. dot com slash WSB. All right, back to the show. So there's a short really neat story about this in the book. And what one of the super broadcasters have done is to program an algorithm so that in his news feed he will get like a random selection of news articles and he can't see where it comes from so he won't be biased. Now, I have no clue how you program anything like that, but I just love that story.

Starting point is 00:15:41 It really tells you about something that it's really your mindset and your approach to forecasting really makes the difference. It just shows you how unbiased these super forecasters, they know they're influenced by this. And so they've done everything that they can to remove these cognitive biases. They're experts in cognitive biases at the end of the day. That's what I really took away from the book. They understand these things and they've designed the way that they receive information to prevent those biases from impacting them.

Starting point is 00:16:10 And that is such an important takeaway from the book in total. All right. Let's move on to the third chapter and that's called Keeping Score. And what the author talks about in this chapter is that he has found that the more confident experts are, the more wrong they're also. And I kind of like that analysis. Basically what he's saying is that the most famous forecasters, basically the experts are CNTV, their skill is not forecasting. That is that they're really good of telling a clear narrative. And they have this very confident attitude whenever they're telling that narrative.

Starting point is 00:16:43 And it really reminds me of the interview we had with Guy Speer back in episode 14 when he's saying, that he doesn't want to give the narrative of a stock whenever he bought it because he doesn't want to be too attached to it. He wants to be able to sell it whenever he needs to. And he wouldn't like to come up as a flip flop or anything like that if he decides to sell that the next day. So he wants to be as detached as he possible can. All right. So jump into chapter four. This one's titled Super Forecasters. And we briefly mentioned earlier about the study and all that kind of stuff. But the thing that I think's worthy of highlighting here in chapter four is this idea that the super forecasters are people that question everything. And they basically design a roadmap

Starting point is 00:17:27 from a particular event. So think of it like this. We're going back to the North Korea example. The question comes down to is there going to be some type of event, a war or something like that in North Korea? That would be the question, whether that's true or false. And the way that these super forecasters will go through the thought process of this, they will start breaking the question down into subcomponents. And then they're assigning probabilities to these different subcomponents. So for that example, is there going to be a war in North Korea? And what they would do is they'd say, well, let's look at it from a political landscape.

Starting point is 00:18:04 What do we think the probability is assigned to the political landscape? What are we thinking the probability is that North Korea could potentially do something like this to set it off? what would be the implications of China. They would dissect the entire array of potential reasons of how that could eventually happen. And then the corresponding probabilities for each one of those particular events from playing out. And the analysis would be done from a pro and a con of why it could or it could not happen. And so they're very analytical and very organized in their thinking, which I think is very different from the way the typical person approaches,

Starting point is 00:18:43 a complex and difficult problem like that. Because the normal person would, like Stig was saying earlier, they'll latch on to a narrative. They'll maybe hear a friend or somebody else who says, well, you know, China in the past has always done this. So that's why there is not going to be a war in North Korea. And that's the end of their analysis. That's the end of their thought process of how they broke it down. Whereas the super forecaster is going so much deeper and so much more involved in the

Starting point is 00:19:13 way that they're processing all the variables at play for a particular event. And that is so important when you're trying to think through a complex problem in forecasting. So in chapter five, he talks about whether or not super-forecasters are super smart, because it seems very advanced, perhaps, what they're doing. And what he find is that IQ is not fully predicting super-forecasting, even though it is actually important. He's saying that forecasters typically perform really well if they're in the top 30 of the population. And then there's another step until the super forecasters, which are all in the top 20% of the year population. But he said there's something that's even more important than IQ. That is how much they enjoy cognitive challenges. Do they like

Starting point is 00:20:00 to do Sudoku and Crosswords? That was actually his examples in the book. And what is the openness to experiences? And he came up with this example. He said that one of the questions they asked super forecasters was they wanted them to predict the presidential election in Ghana. Now, very few people outside of Ghana would know about the presidential election. So it basically comes down to this. How do you think as a human being? Are you thinking that doesn't concern me? I don't want to spend my time on it?

Starting point is 00:20:29 Or are you thinking, this is a great chance to learn more about Ghana? And if that's what you're thinking, then you have the mindset of a super forecaster. Because as a super forecaster, you need to acquire new knowledge. and you'll need to come up with good realistic thesis about what you think will happen. And there's no amount of hard work that can compensate for you being open to that experience of gaining new knowledge. So the next chapter is chapter six and the title of this is super quant. And what this chapter really gets into is the idea of confidence in some of the predictions

Starting point is 00:21:05 and the probabilities that are being determined by these super forecasters. And so one of the examples in the book, and I'm going to read here from my notes, the example that's used in the book is for financial advisors. People usually trust confident advisors faster when compared to advisors that are less confident. On the face of it, accuracy and confidence may seem different, but they're actually correlated. And for many people, we place so much confidence in this correlation that we exaggerate it unintentionally. So this is a bias that people have that whenever they see a confident person who's maybe saying this is what's going to happen and these are all the reasons why, they immediately put way too much emphasis on the probability

Starting point is 00:21:46 that it's actually going to occur. And so that is a potential defect in our thinking. This is a bias that people have. So to combat this and to overcome this, when you get around a person who is very confident and they're giving you very profound examples on why something might or might not happen. If you can't explain why they're wrong or you can't provide other reasons why that thinking might be flawed, the mindset should be that you don't have enough confidence to understand the counter argument to something and you should probably back down on which you think the probability actually is from that confidence coming from the other person. So let's say somebody gives you a great argument. You say the probability of that happening is 80% based on what they told

Starting point is 00:22:28 you, but if you can't figure out ways to think through why that argument might be wrong, your assessment of the confidence of that needs to maybe be shaped and maybe degraded and brought down because you can't provide other means to troubleshoot that. Okay, so chapter seven is titled Super News Junkies. And this one really revolves around a bias called consistency bias. And this is something that we read a lot about in the Robert Chaldini. books where when a person puts an opinion out there, they have a lot of momentum to keep that opinion because they want to remain consistent in their thinking because there's this

Starting point is 00:23:09 stigma in society that when you change your opinion, you're a person of volatile thinking and that you aren't confident in what you think. And it's definitely viewed from a cultural standpoint as being a liability for a person's behavior. So as a result, most people get hardened into these positions that they have, like I have the opinion that X, Y, and Z is going to happen. Even if they have a ton of proof later that's unraveled

Starting point is 00:23:36 and shown to them that they're wrong, a lot of the times people will even become more hardened in that opinion to remain consistent than they're thinking. So what he's getting at here in Chapter 7, what the authors are getting at in Chapter 7, is that these super forecasters

Starting point is 00:23:53 take a very different approach to this bias. In fact, they're very quick to change their opinion when new information is presented. They're very quick to say, oh, that's really interesting. I think maybe the way I was thinking about this before is wrong. And now I might actually have the exact opposite opinion. That's the hallmark of a super forecaster is that way of thinking. And so a real famous investor that immediately comes to mind for me when reading through

Starting point is 00:24:19 this chapter was Stanley Drunken Miller. Because when Drunken Miller is on TV, I've heard him say, I don't even know how many times. You know, this is my opinion today. I think gold's going to go up. But, you know, I might change my opinion tomorrow, and I might actually have the exact opposite opinion. I might put on the exact opposite play tomorrow if new facts are presented to me. And so that is definitely a person of super forecasting mentality and thought that is being

Starting point is 00:24:45 presented. So this chapter title, Super News Junkies, they're constantly reading the headlines. They're constantly reading both sides of an argument, and they're constantly updating. their projection on what they think the probability of something happening is. So if they had an estimate that it's 63% probable one day, they might read a news article and then determine that now it's shifted to 57% likelihood the following day based off of this information that they got. One final point from this chapter that I think is important to highlight is that

Starting point is 00:25:16 super forecasters are really good at picking apart the critical variables that are driving the ultimate outcome of something. They don't get caught in the weeds of ideas or evidence that's really not going to produce the final outcome. And I think that's a really, really important part. I don't know how a person hones that skill, but I definitely agree with that analysis that they present in the book. A really good quote that he has in this chapter was from the Bridges economist John Mena

Starting point is 00:25:48 Keynes. And apparently he said that when the facts change, my opinion changed. I think it's also really a cultural thing, as you also said before, Preston, because especially in the West, we don't like what we would call flipflowers. We don't like people to change their opinion. We see them as inconsistent. We see them as not having perhaps a good analysis because why are they changing their opinion? I think that's something I thought a lot about. For instance, after we did the first episode about gold with Jim Rickards, I remember thinking, hmm, perhaps I was wrong about how I looked at gold. Not in the sense

Starting point is 00:26:21 as I would like to have it as an investment, but in terms of what does gold mean? It's actually a currency and what that really meant after also reading his book. And the first thing I thought about after reading the book was 30 episodes ago or whatever, I said something completely opposite. And so I was considering, could I really say on the podcast that it changed my opinion, how would people think about it and how would they perceive me? Would they perceive me as a flip-robber? Instead of just being authentic and said, I don't know if I'm right, but I think I'm going to. smarter about this subject and this is what I mean now and this is what I meant in the past and this is why it's different. I realize this point about when the facts change, you change your

Starting point is 00:26:59 opinion. Yeah, I totally agree. 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 on one AI-powered platform. So whether you're prepping for a stock two or running an enterprise GRC program, VANTA keeps you secure and keeps your deals moving. Instead of chasing spreadsheets and screenshots, VANTA gives you continuous automation

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Starting point is 00:30:23 All right. Back to the show. So chapter 8 is called perpetual beta and it's about how it's hard to look back at your previous forecast. It actually turns out that forecasters have a really hard time remembering what they actually predicted in the past, especially if they were wrong. And they actually have this experiment when they're asked forecasters what they thought about a given event and the situation it was the Berlin Wall.

Starting point is 00:30:51 And it actually turned out that they misremembered by a margin of 31%. So that would mean that 71% thought it would fall when it was actually only 40%. And this is the problem we have all as forecasters. We tend to remember the times that we were actually spot on on the predictions and kind of forgetting or coming up with excuses. whenever we're wrong. And we see that in the stock market all the time. Like whenever you're talking to other people,

Starting point is 00:31:18 my fellow investors, you'll be telling you about all the times they were correcting the analysis, whether or not it was because of the analysis. And it was probably also because of bad log and whatnot that the bad investment didn't turn out. So in chapter nine, this is called super teams, and the best way to describe this is the idea of group think.

Starting point is 00:31:36 And I think most people that listen to the show, because I think we've talked about this a few different times, are aware of this bias that, occurs when a group of people get together, one person throws out an idea and everyone kind of feeds off of, and it's almost like confirmation bias as well, where people were feeding off of that idea and they're going in this certain direction, and they fail to go back and shoot holes through the argument of why that approach or that forecast and direction that they're looking to go might be wrong. And the book does a great job describing an example of this, and they use President Kennedy's

Starting point is 00:32:10 administration with the Cuban Missile Crisis and the teams forecast as a group of what they actually thought was going to happen versus how it actually turned out. So I really like the story that they provided in the book. It was a really good example of how this goes wrong in many different ways and ways that you can also go about trying to prevent it from happening, which is really kind of opening up to the group and saying, okay, so who has the opposite opinion? Who sees this from this vantage point? What do you think the probability of this occurring is? and going around the room and kind of forcing those people who normally don't talk to throw ideas. And here's another example.

Starting point is 00:32:47 Going to a person who has a really strong opinion and says, this is what it is. And then going back to that same person and saying, I want to hear you argue the other side of the opinion here. I know you think that this is what's going to happen. But I want to hear you argue the opposite opinion of what you have. And forcing that person to think outside the box. And then whenever you would create that dynamic in the group, which you're going to have is you're going to have, you're going to have everyone else in the room thinking through, oh, well, here's maybe how you could argue the other side of that. And you start getting everybody in the room starting to think

Starting point is 00:33:18 of ideas of how they could argue the other side of it. So much of this is driven by the leader who's basically moderating the discussion. So in this case, it would be President Kennedy. He has such an important responsibility in driving this conversation and most importantly remaining neutral in the way he's accepting the information because as soon as all those subordinates in the room starts seeing that maybe he's leaning in one way or the other, they now immediately start tailoring their discussions and their narratives in that direction because he's their boss. They want to, you know, look good in his eyes. And so that's a really important consideration for anybody in a leadership role to try to go

Starting point is 00:34:03 about this from a super forecaster perspective is to really try to keep things in a balanced argument mode throughout the entire discussion. Yeah, and they really found that diversity is a strength whenever it comes to these super forecasters teams. And it basically comes down to that different people have different ways of collecting the data and how they process it. And I don't know if that's also explains why super forecasters are doing better than intelligence agencies. Again, I don't know anyone in an intelligence agency so I would know, but I would kind of assume that a lot of them would have a more similar background in the teams than what we see in this book, whether just basically conformative background you can think of. Because the most important thing is to

Starting point is 00:34:47 avoid the consensus fallacy. And that is that we are so similar and we like each other. We don't want to have disagreements. So why can we just go with this conclusion and then run with that? On the other hand, a group cannot be too diverse. We can't have too many disagreements because if we see people having too many disagreements, the author also found people wanting to win the argument rather than finding the truth or find the right forecast. It's more like, my argument is better than yours and I don't like you. So they're more looking into it like themselves and like having a sense of fulfillment

Starting point is 00:35:22 from winning an argument or see the shouting mats, then actually come up with a good answer. Boy, is that one true? When you think it through it. I mean, think about it. How many people are trying to save face and it more becomes an ego thing, then let's discover what the truth is and regardless of how that might make me look in the long run. But let's jump straight into chapter 10 because it revolves totally into this conversation, which the title for chapter 10 is the leader's dilemma.

Starting point is 00:35:50 And what it discusses is that the three key characteristics that most people attribute to strong leadership is confidence. decisiveness and vision. And so when you look at those first two words, confidence and decisiveness, it really kind of goes against a lot of the things that we were describing in the previous chapter where the leader needs to be uncommittal. He needs to provide this framework for two opposing points of view to play out, like as if he has no idea what choice he's going to make in order to create that environment of

Starting point is 00:36:29 disparity between the two sides of the argument so that the truth can be unveiled. And so the authors say that although there's this dilemma between being a great leader with confidence and decisiveness, leaders can implement this. They can display these attributes of a super forecaster, but it's very difficult for many leaders, especially ones that maybe have been in charge for a long time and they have this army of staff around them that are accustomed to feeding that. that leader exactly what they want to hear. And it's a really interesting discussion.

Starting point is 00:37:03 I think it's a really important highlight, especially for anybody in a managerial role. So let's go ahead and jump to chapter 11. And this one is titled, Are they really so super? And Stig's going to go ahead and cover this one. So basically in this chapter, he's trying to come up with counter arguments

Starting point is 00:37:17 by his thesis for this book that super forecasters actually exist, why that might be wrong. And he presents the argument from Nasin Telap's book, The Black Swan. We talked about that book in the episode, 47. And the premise for the Black Swan is that just because you can prove is not correct,

Starting point is 00:37:36 doesn't mean it's correct. And the reason why he's called that Black Swan was that he was talking about living in Europe in the 1500s and you could never ever imagine a Black Swan because they didn't exist in Europe. But just because you haven't seen one, just because you haven't experienced it, just because people haven't told you about it, doesn't mean that they don't exist. And he brings up the same premise for his book. He said, like, I have all this great evidence why there's something called Super Forecasters and I think I can identify why I'm right about my thesis. But what can I do to prove myself wrong in the sense that there's probably nothing like Super Forecasters, that this is all wrong? And I think that really shows something about a very

Starting point is 00:38:21 humble attitude that he has to his own work. I think in general at the end of this chapter, the thing that the authors are really getting at is I think they have a deep appreciation for Nassim Taleb and some of the research that he's done with the book Black Swan and from a statistic standpoint. But I think that they disagree with Taleb where Taleb really kind of writes off that anyone who's trying to make forecasts in the future, you know, this is probably how Nassim Taleb would say is an idiot. And I think they have a much more positive outlook in their research through these super forecasters that it can be done. with a very high level of predictability and confidence from the people that are very good at it.

Starting point is 00:39:04 So I just see that they have kind of a conflicting point of view, and that was highlighted in the 11th chapter of the book. Moving on to the final chapter, which is what's next. It's just kind of a really quick recap of the book. The authors talk about how forecasting is really important for a business's success and for really a government success in order to accurately predict what's potentially on the horizon. and he talks about if you use the framework that's outlined in the book, you're not going to get perfect results,

Starting point is 00:39:35 but you'll have a framework for keeping track of what your forecasts have been. And that was a really important part in the book is don't just make a bunch of forecasts and then don't look back at what the track record is, because you have no way of actually determining whether you're in this category of super forecaster without keeping a track record statistically proving whether you're an outlier or not. I think that that's a really important consideration is the historical reference of how well a person has done it after making 100 or a thousand different forecasts. And using evidence-based policies in order to develop the framework for how those forecasts are being conducted. So he says that if you're going about it in that manner, it could actually be very fruitful for the individual who's invested a lot of time and effort into honing this skill.

Starting point is 00:40:24 And I completely agree with them. So that's our analysis of the book. I really enjoyed this. I found it to be really interesting. If you're a person who isn't aware of a lot of biases, this would be a great read for you to kind of get caught up on maybe where there's some flaws in your thinking, especially when it comes to assessing where investments might be going into the future. And if you're interested in reading our executive summary of super forecasting, you can go into our show notes or you can sign up for our email. list when we send out PDF files with the executive summaries twice a month. But guys, that was all that we had for this week's episode of The Investors Podcast. We see each other again next week. Thanks for listening to TIP. To access the show notes, courses or forums, go to theinvestorspodcast.com.

Starting point is 00:41:13 To get your questions played on the show, go to Asktheinvestors.com and win a free subscription to any of our courses on TIP Academy. This show is for entertainment purposes only. Before making investment decisions, consult a professional. This show is copyrighted by the TIP network. Written permission must be granted before syndication or rebroadcasting. TIP, TIP, TIP, TIP.

We Study Billionaires - The Investor’s Podcast Network - TIP146: Superforecasting - The Art and Science of Predicting (Business Podcast)

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