Episode 903 Scott Adams： Good News is Breaking Out Everywhere. Swaddle Up and Get Some.
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Summary
In this episode of the simultaneous swaddle, I talk about the coronavirus crisis, the potential for Kamala Harris to become the 2020 Democratic presidential candidate, and why we should be worried about it. I also talk about some other things.
Transcript
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Hey everybody, come on in here. It's time for the simultaneous swaddle. It's that bonus
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entertainment you get, commercial free if you're seeing it live. And it's coming at
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you with a ferocity matched only by a tiger with coronavirus. Yeah, you need to add a tiger to the
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coronavirus to get this ferocity. So today I'm going to show you a little bit about reality that
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some of you may have not been exposed to. You know how I'm always talking about if you have a good
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talent stack, you can see situations a little more clearly. You can see in more different windows
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if you have experience in different fields. So I'm going to give you a little glimpse of what I see
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when I look into this whole coronavirus situation here and see if it matches what you think. Now,
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for those of you who have similar backgrounds, you're going to say to yourself, I knew that,
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but you might get a new way to explain it to other people. So stay around for that. And for some of
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you, you're just going to be sort of surprised. And you're just going to say, I'm not even sure
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that's real. But we'll find out. But first, some things. In the news happening just recently,
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did you hear that Japan is going to reportedly, I think I would wait for confirmation on this, but
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reportedly Japan is going to pay its own firms to leave China? They're going to pay them to leave.
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Ouch. I think China is going to have some serious problems. Have you noticed that everybody's talking
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about Kamala Harris for vice president, vice president candidate with Joe Biden? Look how
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close we are. We're so close. Because even if you don't think she would be the best choice,
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would you at least agree that the consensus has formed around her? Would you at least agree that
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other people have sort of decided, because she's leading on predict it? You know, in the betting
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poll, she's number one for getting the spot. Then Biden has signaled it about as clearly as you
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possibly could. So you realize that I'm on the verge of the greatest prediction of all time.
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But it still might not happen, so I don't want to get too cocky. Anything could happen. If it doesn't
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work out, I'll be the first one to admit it. But it's so close. I don't know if you've noticed,
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but there are some really hopeful signs. Number one, the number of hospital admittees, I guess,
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went way down in New York. Number of deaths went up, but I think that's a trailing indicator,
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meaning those are people who are already in trouble for a while. But if fewer people are going
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into the hospital, what's that mean? What's it mean? Is it because of hydroxychloroquine? Is it
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because of social distancing? We don't know, but it's a good sign. And everybody's talking about the
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curve bending, and maybe things are going to head in the right direction. But there's another good
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sign, one that we've all been waiting for. My town has toilet paper. I'm getting reports from other
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people in other places that they too do not have fully stocked shelves, but that at least in some
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stores, they too have the great white, the great white hope. And so normality is returning. Approximately
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when I told you it would, a month after the toilet paper disappeared, I said to you, give it one month.
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It'll be back. It'll be back. And here it is. So let's talk about some other stuff. Oh, poor Drucker
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died. If you don't know who he is, that won't mean as much to you. Here's a question for you.
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When we're done, when we're on the other side of this coronavirus, there'll be the heated debate
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of whether the number of deaths was lower than predicted because we did such a good job
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or because it was never dangerous in the first place. So in order to anticipate that problem,
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that we're not going to know why we didn't have a lot of deaths and people will disagree,
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could we agree in advance who our comparable is? In other words, could we find another country
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who is sufficiently like us, or maybe a few of them, who are doing something different? Let's say
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they're not doing social distancing and they're, let's say, not testing because we didn't have that
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option. And they're not wearing masks, at least all of them, because, you know, we didn't have that
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option either. So if you're trying to find somebody who is doing something smarter than us,
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you'd have to find somebody who's not doing much testing and they're also not doing much social
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distancing and they've already got, you know, a good amount of infection. And then that would be
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our comparable, right? Am I, am I right? So I don't know which, which country that is, but let me put
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that out there. That if, if anybody is claiming that they're going to wait until we're on the other
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side of this and say, see, it's self-evident that it's because of the social distancing or
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it's self-evident that that made no difference. It was no problem at all. It's not going to be
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obvious just by looking at it. You're going to have to have some standard to compare it to, to
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even get a, you know, a hope of understanding what happened. All right. Here's my little whiteboard
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lesson for today. This is what I call the classic view of reality. This is the view that we think
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should be the view or that should be the way reality works. If you are young, you may have learned that
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this is what reality looks like. You've got your experts. They consult with your leaders. Your
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leaders make some policies. And then those policies rain down on the happy citizens who are happy that
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experts were part of the process and that leaders took their advice. And what a good world we live in.
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So that would be the classic view of the world.
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On the other side of my magic whiteboard that is so incredible, you probably all want one, but
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you can't have it. It's one of a kind. Here's a more accurate view of the world. It goes like
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this. You've got your experts. And let's say that there's a situation that comes up that they
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have not seen before. Let's take the coronavirus situation. Now they've seen other pandemics and
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other infections, but they haven't seen this one. So I think you'd agree that this was a brand new
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thing and there was a lot of fog of war and they didn't have good information at first. But they're
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the experts. Even though they don't have good information, they still have to inform the leaders
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what to do because doing nothing is also a decision. So the leaders have to make a decision
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to do nothing or to do something very expensive and aggressive. And they're not going to make the
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decision on their own. And the leaders aren't going to be able to get away with saying, well, we really
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don't know. There's not enough information. If you would just let us wait a year, no, we can't wait a
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year. It might be too late. So the experts are forced to make a decision well before they have
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anything that would satisfy themselves of what to do. So what do they do? Well, here's what I believe
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happens in the real world. You've got lots of experts, but they're not all equal. Some experts
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are more influential. Let's say Dr. Fauci, for example. So there are probably several experts in the field
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who are recognized as the most important ones. So probably it only takes half a dozen or maybe
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fewer than 10 of the top experts to agree that they have an instinct for something or they feel
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there's a problem or this could be really bad. You know, that all of their experience, their pattern
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recognition, their biases, you know, good and bad. I'm not even saying any of this is bad. I'm saying that,
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you know, an expert is not just the data. And if they don't have data, at least not reliable data,
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they're going to take advantage of their pattern recognition and bias. But also very importantly,
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they don't want to kill anyone. That's going to be job one, right? And that's a really very
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important bias because they're going to make sure that nobody can blame them for killing anybody in
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particular. That would be anybody's natural impulse. Now, of course, they're also professionals.
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So they're going to try to be as independent minded as possible for the greater good.
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But put yourself in that position. Would you ever recommend something that has a pretty good chance
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of killing a million people? If you get it wrong, you're going to want to do whatever's the safest
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in terms of not killing a million people and not taking any chance that you're the one who made
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some dumbass recommendation and killed a million people. So professional as they may be,
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they're still humans and nobody wants to kill a million people. So you've always got that force.
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So what do you do if you've got a really strong feeling that that something is bad, but you don't know?
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And let me give you a little sort of math example of why they would be confused in the early days of
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the pandemic. Let's say that the thing that makes a virus powerful is just, I'm oversimplifying a little
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bit, is how viral it is and how deadly it is if you get it. So two different things, how quickly it
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spreads. And then if you get it, what are your odds of dying? And so in very rough terms, you could
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sort of multiply them, multiply them to see how bad it is. So if you had, let's say, a very low
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spreading, but it was very deadly, well, that wouldn't be the worst thing, because you might be
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able to stop it, because even though it's deadly, it's not going to spread that far. So multiplying the
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low virality times the, you know, high death rate gives you some power number. But you can get to
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that same power number by having something that's super viral and doesn't kill many percentage of
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people, but it gets so many people that even though it's a low percentage, a lot of them die. So both of
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them could be equal power, one because it's very viral, and the other because it's very deadly.
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But what happens if you get something that's super viral, and by the worst turn of fate, super deadly?
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I don't believe we've had one of those. Maybe the Spanish flu qualifies, but I'm not even sure it was,
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that one was, you know, the super deadly, super viral. I'm not sure how that gets classified. But
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in the earliest days of the pandemic, was there enough information that the experts could know
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if they had something that was very viral, but only a little bit deadly? Or something that was
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a nine, nine out of 10 in virality and deadly? They didn't know. They were looking at China
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and things look bad, right? So what do you do if you're the experts and you've got to advise the
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policymakers what to do? Either to do nothing or do something, and you don't have any information.
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Well, you're going to take your best instincts and judgment, your risk management, your not wanting
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to kill millions of people, and you're going to have to convince leaders who are going to ask you for
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your data. And what do you do? So the president says, okay, you know, this is important. I get it.
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Now show me your numbers. And what do they have? Well, not much of anything, right? Because they just
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don't have good numbers. They have guesses. They have assumptions or lots of variables. It's really
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all very complicated. Then what does the president do? How do you convince the president, even though
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you're the experts, and all you have is hunches and guesses and patterns and things from the past that
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were similar? You've done some math in the back of an envelope. Can you convince the president? And
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could you convince the public with something like that? Could you go directly to the public and say,
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look, we can't prove it, but a whole bunch of us who are experts have a really bad feeling about
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this thing? And we don't want a million people to die. So could they convince the public? No,
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because the public would say the same thing. Based on what? Show us your proof that this is so bad.
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And they would say things like, well, there was this anecdote, and there was a story, and there's
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this unreliable information. There's this pattern of something that was different. There's this time we
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thought we were right, that we were wrong. And when they're done, you just say to yourself,
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I'm not sure I'm convinced any of this is real. Yet, yet we still have to make decisions.
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So how do you solve this situation in the real world, where it's not that classic view that's
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on the other side of the whiteboard, where the experts know just what to do? They look at their
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data, they look at their spreadsheets, it all adds up. They tell the leaders, the leaders say,
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hey, that looks good to me. Thank you for looking into that. And they make their policies. In the real
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world, that just doesn't work that way. Even the experts don't know what to do, if they don't have
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data, and it's the fog of war, and it's a brand new thing. Now, someday, we might understand enough
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about this, that if we could have traveled back in time, with our future knowledge, well, we maybe we
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would handle this differently. But even the experts didn't know what was going on. Thank you, China,
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right? So here's what you do in this situation, where there's something you feel really strongly
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about, you're pretty sure there's a lot of evidence, but you can't communicate it. You just can't sell it.
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And so you go to the model makers. And here's where everybody in social media and on the news
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is making a fundamental misunderstanding. And it's this. You don't use models to give you the answer.
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Models do not produce information. They're a sales tool. They're for persuasion. How do I know that?
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I know. That's the opposite of what you think, right? You think that the experts are uncertain.
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So they use a model to become certain and to learn something. That's probably what you thought,
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right? Ask anybody who's ever done this for a living. That's not what happens. Here's what really
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happens. The experts say, go make us some models because I got to sell this thing. I need a picture.
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Give me a graph with a scary looking curve. Give me a few big numbers. Give me a range and make it a
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big range because I don't want to be wrong. All right? Whatever you do, make it a big range.
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So the model makers do their magic and they come up with some really scary looking graphs that are
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really simple that they can sell to scare the public and it's compatible with the experts. Now,
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what would happen, hypothetically, if the people making the models came up with a prediction
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that would kill people if it were wrong? What are the experts going to do? So, for example,
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let's say the experts are looking at the carnage over in Wuhan and they're saying to themselves,
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I know there's something bad out there. There's definitely something bad out there.
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This is not normal as far as we can tell. We haven't confirmed it, but we're seeing a lot of
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stuff that just doesn't look normal, you know, spraying of the streets over there and everything.
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So what happens if the model makers said, well, we've put in all the variables, we've checked all
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of our assumptions, and the very best model we've come up with says it's not much of a problem.
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So it doesn't look like it'll be a problem over here. So what do you do if you're the expert?
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Because that model would disagree with what you plainly can see. It disagrees with your sense of
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risk management. It disagrees, you know, with observation, instinct, hunch. It just disagrees with
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every fiber of your being. Are you going to say to the model maker, oh, thanks, I was worried before.
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But now that you ran the model, feeling better. No, they discard that model. Because you can't,
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you can't keep the model that says it might not be a problem. That's the one that could kill people.
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And remember, job one, job one is don't kill anybody. So the experts can't accept a model
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that is too, let's teach, too not scary. Because it would put the experts in the position of maybe
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not warning the public about whatever they're seeing in China, that doesn't look good. Right?
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So they would have to reject anything that made it look like it's not a problem. Right? So let's say
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the experts come back and they say, it could be this big range, 2 million, or 100,000. But let's say
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the first time they come back, they say, you know, it's 2 million, or honestly, it could be 10,000.
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What if that was the first model that came back? And the experts say, um, we've got a problem here.
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If it's only 10,000, or even if 10,000 is even in the range, Americans are going to think
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it's not a problem. Because we're used to having things that are a huge risk, but most of the time
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you're fine. If you tell the Americans that even within that predicted range is maybe just 10,000,
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it's like the common cold, basically. How are they going to act? They're going to act in a way
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that they're not taking it seriously. And then what? And then I've killed all these people
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because they didn't, you know, potentially, it could be worse than we think. So here's what
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happens. Models are simply ways that experts can convince leaders and the public that there's
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something that needs to be taken seriously. Now you're saying to me, I'm not buying this, Scott.
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I'm not buying this. Everybody on TV, all the scientists, all the experts, they really sort of act
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like this is telling you something. Like it's actually giving you a window into the future.
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That's the whole point of this. We're spending millions of dollars, you know, billions of dollars
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making them. They're all these models. It's the best, it's the smartest people in the world, Scott.
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But did you forget you're a cartoonist who has a degree in economics and an MBA? Did you forget
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that? Because the smartest people in the world, they're not talking like you're talking. No, they're
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not. But here's my simple argument for why they do not glimpse the future. It goes like this.
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If you have a situation that is really fundamentally like other situations, then sometimes yes. Let's say
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you're into construction and you've built several homes that are comparable, you probably could put
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together a budget that says, ah, based on these last three homes I built in the same town that are
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about the same kind, this square footage, yeah, I'll just estimate this next house will be that.
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That'd be fine. But that doesn't work with a brand new situation. There's no way to estimate it.
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And if anybody could use any complicated model to predict the future, even statistically better
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than guessing, they would not be making models. They would be sitting on their trillion dollar
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yachts. If anybody could do this in actuality, like actually predict the future with some statistical
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validity whatsoever. They'd do it for stocks, right? They'd do it for knowing what startups
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to invest in. They could make bets on big events. In other words, they could bet that there'll
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be a, you know, tragedy or a forest fire, you know, this or that. If anybody could look into the fog
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of war, which is what was happening in the beginning of the pandemic, if anybody could look into that
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and build a model on a spreadsheet or with any kind of software that could actually reveal the future
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better than chance, they would be almost magic. And there's no such thing as magic.
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So this is, this is me letting you peer behind the screen. Part of the reason I know this is that I did
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financial projections for a living. And I can tell you that if my, if my model and my prediction did not
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give management what they felt was the right thing to do for personal reasons, professional reasons,
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or the good of mankind, I had to go back and tweak it until it did. Because we didn't know what was the
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right thing to do. But we knew that the model wouldn't tell us. We knew that the variables were,
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the unknowns were so extreme. For example, one of the things I was trying to do is calculate
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whether this technology that's, you know, obsolete now, but was new then called ISDN,
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would have a big future and whether it was economical to put it into the network and sell it to people.
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And there were just gigantic unknowns about where the technology would go and what would happen
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with costs. And if you tweaked any one of them, you just get wildly different results. It was either
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a great idea or a terrible idea. It was my job to calculate that. And so it was, was that in other
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situations where I learned that you start with the answer. And the answer was, yeah, we're a high tech
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company. We have to be investing in high tech stuff. Don't bring me back some financials to say we don't do
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that. Because we're going to do that. We have to do that. It's, you know, it's, you know, our
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experience, our pattern recognition, our bias, all of our external and internal incentives tell us
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we're going to invest in the new technology. And it doesn't matter, bean counter, if you come back
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and tell us it's a bad idea, I'm going to tell you to go back and tweak a variable until it's a good
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idea, because that's the business we're in. We're in this business. We're not going to be not in that
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business because your spreadsheet said so. So once you see behind the curtain, you realize that the
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models are always just an expression of what the experts or the leaders want them to be. And they
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are sales tools. They are not things that tell you what is true. So for all the people who are not
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quite seeing behind the curtain, you can see them debating in public. You can see it on TV, you can see
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it on social media, and they say things like this. Those models were inaccurate.
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Doesn't mean anything. If you actually understand what the models are for, and what is possible,
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it is meaningless to say the models turned out not to be accurate, because they were never built to be
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accurate. You can't be accurate. In the same way that the models were not built to crap bars of gold for
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all of us. The reason is, it would be great if you could build a model that would crap gold. Wouldn't you
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do it? I mean, why not? You're a nice person. You'd give us all a little gold with your algorithm. But you
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can't really build a model that can predict the future. It's not a thing. So you can't be mad at the people
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who built the model for not doing a thing that's not a thing, which is predicting the future, from unknowns.
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Again, if you had a situation where it was very similar to the last one, that's different. But in the fog of war,
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nobody can predict. It's not a thing. So the people who understand the least, and I would argue people
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who have never been around financial predicting, people who are not economists, maybe people who
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are not scientists, maybe journalists, if I may say so. I think the journalists and the artists
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are more likely to think they don't understand what's going on, because the predictions were so far off
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what we're actually experiencing. And I think to them, they're saying, well, was this a conspiracy?
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Was everybody stupid? And I don't think it was a conspiracy. And I don't think anybody involved
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was stupid. And I don't think anybody involved was acting with bad intent. I think everybody involved
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used the only tools they could to get a result they thought would protect people the most,
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and maybe with a little bias toward making sure they were not the ones who killed a million people
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personally. So I think everybody had good intentions and used the tools they had. But don't be fooled
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into thinking that the models tell you what the future is. That's just not a thing. But they are good
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for persuasion. And you can see how well they work. Now, having observed this situation, and I think
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I succeeded in at least convincing some of you that the way it works is not that models tell you
00:28:02.480
something. It's pretty much the experts tell the models what the models need to say in order to get
00:28:07.920
to the next level, if you will. You can see why some people have a problem with the climate
00:28:14.780
models. And I don't want to get into that. I just want to alert you that it's hard to form an opinion
00:28:23.400
on one of these things without extending it to, well, does that apply to climate change? And so here's
00:28:31.580
what I would ask you. The climate change models, again, would be accurate if they are similar to
00:28:40.660
things that have worked before. In other words, if we were good at modeling climates, and this was
00:28:47.380
just another one, maybe we'd be good at it. But we've never modeled a climate 80 years in advance
00:28:55.680
using the tools that we're using. We've never done it. We think we're doing it now, but there's nothing
00:29:01.700
to, you know, to test it against. I would suggest that the best way to understand the climate change models
00:29:10.520
is this way as well, which is that the scientists genuinely believe, based on everything they've seen,
00:29:19.480
every scientific test, every paper, their pattern recognition, the people they've talked to, just the whole
00:29:26.320
weight of their experience, I think, is screaming to them, we're in trouble, the planet's going to warm
00:29:32.300
up. But they don't have the ability, because it's impossible, it's not because they're bad at it,
00:29:38.160
it's just impossible, to communicate all the things they know, you know, the full weight of their
00:29:43.040
knowledge, to a leader, or to the public. Because it just doesn't transmit, it would just sound like
00:29:50.340
gobbledygook, and we'd say, I don't know, I'm not even sure you know what you're talking about.
00:29:53.700
And so, the scientists do the same thing that you're seeing here, which is they go to a model,
00:30:00.260
for them the model is really persuasion, but the public thinks the model is actually information.
00:30:06.680
The public thinks it's an actual prediction. Now, it might be a prediction that the temperature
00:30:11.280
is going up, and, you know, there's a risk, but the actual model, we'll see. And by the way,
00:30:20.200
the model can be right, if they make the range big enough, if you make the range big enough,
00:30:25.460
there's a pretty good chance it's going to fall in that range. So, that is my lesson for today.
00:30:30.920
I hope that was useful. Was it? I would like to see your feedback. I'll stay just for a minute to
00:30:39.540
look at that. Is Bill Gates in here? I don't know what you're talking about. Who wins? Big business?
00:30:52.160
Well, I don't know if anybody's winning. Very few people are winning.
00:30:57.340
So, I see more people asking about Dr. Shiva. Yeah. So, I'm still waiting for somebody to make a statement.
00:31:11.020
I'm waiting for somebody to make a statement that they think they would like my opinion on
00:31:16.540
that is something that Dr. Shiva says. So, narrow it down to me. Not just like a whole topic,
00:31:26.540
but like, is there a statement or a fact? Just narrow it down, and I'll give you an opinion.
00:31:33.800
Post your whiteboard on Twitter. Yeah, I'll take a picture of it as soon as we get off here.
00:31:39.760
Oh, good. Actually, the feedback is better than I thought.
00:31:42.280
I didn't know if that was going to work as well as it did. How delightful.
00:31:50.900
So, it doesn't seem to me that you treat climate models like this.
00:31:55.100
Should they be treated like this? Well, I just did.
00:32:05.520
I wouldn't watch you if I didn't like your take on things. That's true.
00:32:13.840
Well, I'll tell you, models were ruined for me when I became the person who made them.
00:32:21.840
Nothing will disillusion you more than being the person who's doing that thing.
00:32:26.420
If you want to be unimpressed with something that you used to be impressed at,
00:32:32.560
try being that person. Let me give you a concrete example.
00:32:36.360
When I became a cartoonist, I immediately set my sights on winning the top award in cartooning.
00:32:44.760
Because I thought, you know, if you're an actor, you probably daydream of getting the Academy Award, right?
00:32:51.780
So, whatever business you're in, you probably spend a little time daydreaming about winning the Super Bowl
00:32:58.240
or getting the gold medal or something like that.
00:33:00.640
So, I wanted to win this top award in cartooning.
00:33:05.440
It was called the Rubin, named after Rube Goldberg.
00:33:10.260
And every year, the cartoonists get together with sort of an Academy Awards,
00:33:14.000
and there's a vote, and they give the top award to a cartoonist.
00:33:18.920
And years go by, something like 10 years, and I'm not even nominated.
00:33:23.300
And Dilbert just takes off and gets financially successful.
00:33:29.820
So, suddenly, Dilbert is like one of the biggest things in the country.
00:33:42.140
I don't think the comic is that much better than it was a year ago,
00:33:48.720
But what is different is that events and society sort of propelled me in front of a lot of people,
00:33:56.900
So, the thing that was different is I was making a lot of money and getting a lot of attention.
00:34:01.240
And I thought to myself, what kind of award for cartooning depends on how much PR I'm getting?
00:34:10.620
And so, I actually ended up winning the two top awards in cartooning.
00:34:16.580
One is the overall award, you know, the biggest one you can get.
00:34:19.920
And then the top award in my category, which was cartoon strips.
00:34:26.920
And, of course, I was, you know, terribly honored and everything.
00:34:34.800
the entire thing seemed like a farce and didn't mean anything to me.
00:34:38.600
Because once I got on the inside, and I was the actual winner of it,
00:34:43.560
I just realized it wasn't so much because of my cartooning.
00:34:50.980
And the organization that puts on the event likes to have name brands.
00:34:58.780
It just makes the event better if you've got, you know, some famous people there.
00:35:01.640
So, I had won the award simply as a way to get me to attend the event.
00:35:10.160
And I spent years of my career, like, lusting after this award
00:35:14.800
that when I won it, I realized was completely meaningless.
00:35:20.000
And if there's anybody out there who has won that award, sorry.
00:35:28.000
But my college, my undergraduate college did the same thing.
00:35:33.160
When I got famous, they made me alumni of the year or something
00:35:36.740
and invited me to go back and give a talk and everything.
00:35:42.480
I'm alumni of the year of the little college I went to.
00:35:46.940
I was like, man, of all those people, won't they envy me?
00:35:50.740
My old classmates, when they see that I'm the alumni of the year.
00:35:56.360
And then you go back and you realize it's just a fundraising thing.
00:36:00.980
You know, once you get famous for doing something,
00:36:03.920
you're alumni of the year so that they can have more access to you for fundraising.