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That's my idea of antifragile, right? Because I didn't start as an academic. I started in the real world.
Yes. I mean, look at it now. I mean, I started when Gaza started, I felt honorable to go in and defend the Palestinians when nobody was defending them. It took a while for a lot of people to jump on the train. And in the beginning, I had probably 15 people attacking me for every one person supporting me. And now, of course, it has switched because maybe they found it less effective to attack me. They can't intimidate. People tend to attack those who can be intimidated. So there's this sense of honor, you know, that as you advance in age, if you're doing things right, you go back to your childhood values, you know, we're about honor and taking a stand when needed.
It's more effective to make money slowly because people like to make a million dollars a day for a year rather than 250 million and then nothing. But it's a reverse for losses. But there's a difference that in the real world, you don't know the odds and you don't know the payoff function very well. In your world, you know the odds and the payoff functions. One has to be pretty blind not to see that you have winner-take-all effects in finance, which is not compatible with a Gaussian representation. People get blinded by theories, and also because if you're trading your own money, you're going to be pretty rational about it. If you're dealing with the institutional framework, you need to make money frequently. And the trap of needing to make money frequently will lead you to eventually sell volatility. So there's no incentive to buy volatility for someone who's employed for finite periods of time in a firm.
The way you need to look at venture capital is that it's largely a compensation scheme, like a hedge fund's compensation scheme mechanism. So they don't make their money, venture capitalists, they don't make money by waiting for the company to really become successful. They make their money by hyping up an idea, getting new investors, and they're cashing in as they're bringing in new investors, which, I mean, it's plain. Look at how many extremely wealthy technology entrepreneurs are floating around while not having ever made a penny in net income. You see, so the income for venture capital comes from a greater fool approach. You're selling hope. You package an idea. It looks good. So you sell it to someone, and then they have a second round and third round. They keep it around so you can progressively cash in. It's not based on your real sales or your real cash flow, particularly in an environment with low interest rates where there's no penalty for playing that game. They have skills, but most of their skills are in packaging because they're trying to sell it to another person. It's a beauty contest. So they package a company, and look at the competition of these venture capitalists. You can see it. I mean, you have either financing rounds where someone cashes in at a high price, or you have an initial public offering. So I come from old finance, old-school finance, where you haven't really succeeded until the company gets a strong cash flow base.
If now, if I were to structure this conversation about the defects of behavioral and cognitive sciences as linked to economics and decision theory, we have things linked to misunderstanding of structure and things linked to misunderstanding of the dynamic aspect of decision-making, what we call erodicity.
Right? So let's put them in these categories. So we have the equity premium bias comes from equity premium. The fact that people don't invest their explanations come from poor understanding of probability structure. The aspect of prospect theory that is wrong comes from misunderstanding probability structure that if you have an open-ended distribution with fat tails, then you have the same result. The fact that people, if you give them 10 choices, the one over n is optimal under fat tails. You know, you should reduce people's choices because they spread them too much. But that's an optimal strategy. There's another one about probability matching where you think that probability matching is irrational. Probability matching means that if something comes up 40% of the time and something comes up 60% of the time, that you should invest 100% of the time in the higher frequency one. But in nature and in animals, but also humans do probability matching. And when you write the math using entropy, so these are the errors linked to probabilistic structure.
There's another one also. There's intertemporal choices, like if I tell you, do you want a massage today or two massages tomorrow? You're likely to say, okay, two massages tomorrow. Or let's assume that when facing this choice, you take the two massages tomorrow, not one today. But if I tell you in 364 days, you have a choice of one versus two, you Okay, you would reverse. No, you're possibly actually, let's say that you have it the other way, that you take one, the one today rather than two tomorrow. But you reverse that's not if you use a different probability distribution or different preference structure, right? Plus, there is another one that what I mean, how do you know the guy, the person offering you that bet will satisfy tomorrow? You see, as I said, the bird in the hand is better than some one in a future on some tree. Okay. So if you say the person is full of baloney, all right, maybe full of baloney, I'd rather have one today. Okay, let me take it today. Or he may be bankrupt. But if he is 364, 365 days, the effect is not that big. So it depends on what kind of preference structure you have or what kind of errors you have in your model. So this is the first class: misunderstanding probability, and we can go on forever.
The second one is more severe: misunderstanding of dynamics. Like we had a Twitter fight with Taylor while running a Rury where he couldn't understand why you can refuse a bet of 55% win versus 45% probability of losing, that someone can refuse such a bet and be rational. Okay. Well, number one is realize that of course you can refuse such a bet because you got to look at things dynamically. Yeah. If you keep taking those bets up, you know, I take risks in life of that nature all the time. Yeah. You see, so and it would bring you closer to an endpoint. Yeah. I could probably do it for a dollar, but maybe not $10 or not $100. Certainly not a million dollars. Yeah. See, so he couldn't understand the ergodicity. And that's so Kelly Criterion shows it clearly. But Kelly Criterion is just one example of getting that result without optimizing for growth. My whole idea is surviving.
It's like it's simple like saying, "Hey, you know what? The trade-off is smoking one cigarette. Look at how much pleasure you derive versus how much risk you're taking." So it's irrational. So yes, but do you know people who smoke once? You know, you got to look at the activity, not an episode. There are other similar examples. Oh, let's talk about mental accounting. Say a husband and wife have a joint checking account. The husband visits the department store, sees a tie, doesn't buy it, it's too expensive. Goes home and then sees this gift and gets all excited that he got it from his wife for his birthday. Okay. So, you know that mental accounting is irrational. Say, but how many birthdays do you have a year? Okay. Yeah. So it's not frequent. So you know, so this is where you got to put some structure around the mental accounting. Another mistake he makes there, not some mistake. The mistake is that it's irrational when you go to a casino to increase your betting when you win money from a casino. That's mental accounting. That money you won from a casino should be treated from an accounting standpoint the same way as money that you had as an initial endowment. Okay? You think about it. If you don't play that game, it's going to go bankrupt. This is what we call playing with house money.
So it's not R. So practices that have been around for a long time are being judged by that industry. I call it an industry because like we became an industry just producing papers. And they don't have a good understanding of the real world. So and not a good understanding of probability theory.
So if I take a Gaussian distribution and take the exponential of the variable, you see because you know that the log is additive, right? Okay. Okay. So when you multiply, so you take the exponential, you get a log-normal distribution, and the mu and sigma of log-normal distribution are preserved, right? They're not the mean and variance of the log-normal. They're mean and variance of the log of the log-normal. Okay. It's misnamed. It's really exponential. But there was another name called exponential for another distribution. So Gaussian you exponentiate, you get log-normal. Now there's a distribution that's thin-tailed but slightly fatter tail than Gaussian, barely. All right. The exponential, the gamma, you know that class. Okay. You exponentiate. What do you get? A power law. You see? So you're very So which one you're exponentiating? Your base distribution needs to be Gaussian for you to end with a log-normal, right? Or fatter tail than Gaussian. Okay. And the next class is a gamma or you know the exponential, and you get a Pareto. Right. Yeah. And then of course, there's an exponential of a Pareto. Log-Pareto.