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April 10, 2020 - Real Coffe - Scott Adams
36:13
Episode 903 Scott Adams: Good News is Breaking Out Everywhere. Swaddle Up and Get Some.
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Bum bum bum bum bum bum bum bum bum bum bum bum bum bum Hey everybody, come on in here It's time for the simultaneous swaddle.
It's that bonus entertainment you get, commercial free, if you're seeing it live.
And it's coming at you with a ferocity, Matched only by a tiger with coronavirus.
Yeah, you need to add a tiger to the coronavirus to get this ferocity.
So today I'm going to show you a little bit about reality that some of you may have not been exposed to.
You know how I'm always talking about if you have a good talent stack.
You can see situations a little more clearly.
You can see in more different windows if you have experience in different fields.
So I'm going to give you a little glimpse of what I see when I look into this whole coronavirus situation here and see if it matches what you think.
Now, for those of you who have similar backgrounds, you're going to say to yourself, I knew that, but you might get a new way to explain it to other people.
So stay around for that.
And for some of you, you're just going to be sort of surprised.
And you're just going to say, I'm not even sure that's real.
But we'll find out.
But first, some things.
In the news, happening just recently, did you hear that Japan is going to Reportedly.
I think I would wait for confirmation on this, but...
Reportedly, Japan is going to pay its own firms to leave China.
They're going to pay them to leave.
Ouch. I think China's going to have some serious problems.
Have you noticed that everybody's talking about Kamala Harris?
For Vice President.
Vice President candidate with Joe Biden.
Look how close we are.
We're so close.
Because even if you don't think she would be the best choice, would you at least agree that the consensus has formed around her?
Would you at least agree that other people have sort of decided?
Because she's leading in unpredicted.
You know, in the betting poll, she's number one for getting the spot.
Then Biden has signaled it about as clearly as you possibly could.
So you realize that I'm on the verge of the greatest prediction of all time.
But it still might not happen, so I don't want to get too cocky.
Anything could happen.
If it doesn't work out, I'll be the first one to admit it.
But it's so close.
I don't know if you've noticed, but there are some really hopeful signs.
Number one, the number of hospital admittees, I guess, went way down in New York.
The number of deaths went up, but I think that's a trailing indicator, meaning those are people who are already in trouble for a while.
But if fewer people are going into the hospital, what's that mean?
What's it mean? Is it because of hydroxychloroquine?
Is it because of social distancing?
We don't know, but it's a good sign.
And everybody's talking about the curve bending and maybe things are going to happen in the right direction.
But there's another good sign.
One that we've all been waiting for.
My town. It has toilet paper.
I'm getting reports from other people in other places that they too do not have fully stocked shelves, but that at least in some stores, they too have the great white, the great white hope.
And so, normality is returning.
Approximately when I told you it would, a month after the toilet paper disappeared, I said to you, Give it one month.
It'll be back. And here it is.
So, let's talk about some other stuff.
Oh, poor Drucker died.
If you don't know who he is, that won't mean as much to you.
Here's a question for you.
When we're done, when we're on the other side of this coronavirus, there will be the heated debate of whether the number of deaths was lower than predicted because we did such a good job or because it was never dangerous in the first place.
So in order to anticipate that problem, that we're not going to know why we didn't have a lot of deaths and people will disagree, Could we agree in advance who our comparable is?
In other words, could we find another country who is sufficiently like us, or maybe a few of them, who are doing something different?
Let's say they're not doing social distancing, and they're, let's say, not testing because we didn't have that option.
And they're not wearing masks, at least all of them, because we didn't have that option either.
So if you're trying to find somebody who is doing something smarter than us, you'd have to find somebody who's not doing much testing, and they're also not doing much social distancing, and they've already got a good amount of infection, and then that would be our comparable, right?
Am I... Am I right?
So I don't know which country that is, but let me put that out there.
That if anybody is claiming that they're going to wait until we're on the other side of this and say, see, it's self-evident that it's because of the social distancing, or it's self-evident that that made no difference, it was no problem at all, it's not going to be obvious just by looking at it.
You're going to have to have some standard to compare it to to even get a, you know, A hope of understanding what happened.
Alright. Here's my little whiteboard lesson for today.
This is what I call the classic view of reality.
This is the view that we think should be the way reality works.
If you are young, you may have learned that this is what reality looks like.
You've got your experts.
They consult with your leaders.
Your leaders make some policies, and then those policies rain down on the happy citizens who are happy that experts were part of the process and that leaders took their advice, and what a good world we live in.
So that would be the classic view of the world.
On the other side of my magic whiteboard that is so incredible, you probably all want one.
But you can't have it. It's one of a kind.
Here's a more accurate view of the world.
It goes like this.
You've got your experts, and let's say that there's a situation that comes up that they have not seen before.
Let's take the coronavirus situation.
Now, they've seen other pandemics and other infections, But they haven't seen this one.
So I think you'd agree that this was a brand new thing and there was a lot of fog of war and they didn't have good information at first.
But they're the experts.
Even though they don't have good information, they still have to inform the leaders what to do because doing nothing is also a decision.
So the leaders have to make a decision to do nothing or to do something very expensive and aggressive And they're not going to make the decision on their own, and the leaders aren't going to be able to get away with saying, well, we really don't know.
There's not enough information.
If you would just let us wait a year, no, we can't wait a year.
It might be too late.
So the experts are forced to make a decision well before they have anything that would satisfy themselves of what to do.
So what do they do? Well, here's what I believe happens in the real world.
You've got lots of experts, but they're not all equal.
Some experts are more influential.
Let's say Dr.
Fauci, for example. So there are probably several experts in the field who are recognized as the most important ones.
So probably it only takes half a dozen or maybe fewer than ten of the top experts To agree that they have an instinct for something or they feel there's a problem or this could be really bad.
All of their experience, their pattern recognition, their biases, good and bad.
I'm not even saying any of this is bad.
I'm saying that an expert is not just the data.
And if they don't have data, at least not reliable data, They're going to take advantage of their pattern recognition and bias.
But also, very importantly, they don't want to kill anyone.
That's going to be job one, right?
And that's a very important bias.
Because they're going to make sure that nobody can blame them for killing anybody in particular.
That would be anybody's natural impulse.
Now, of course, they're also professionals.
So they're going to try to be as independent-minded as possible for the greater good, but put yourself in that position.
Would you ever recommend something that has a pretty good chance of killing a million people?
If you get it wrong, you're going to want to do whatever's the safest in terms of not killing a million people and not taking any chance that you're the one who made some dumbass recommendation and killed a million people.
So professional as they may be, They're still humans, and nobody wants to kill a million people, so you've always got that force.
So what do you do if you've got a really strong feeling that something is bad, but you don't know?
And let me give you a little sort of math example of why they would be confused in the early days of the pandemic.
Let's say that the thing that makes a virus powerful is just, I'm oversimplifying a little bit, is how viral it is and how deadly it is if you get it.
So two different things.
How quickly it spreads and then if you get it, what are your odds of dying?
And so in very rough terms, you could sort of multiply them to see how bad it is.
So if you had, let's say, a very low spreading, But it was very deadly.
Well, that wouldn't be the worst thing, because you might be able to stop it.
Because even though it's deadly, it's not going to spread that far.
So, multiplying the low virality times the high death rate gives you some power number.
But you can get to that same power number by having something that's super viral, It doesn't kill many percentage of people, but it gets so many people that even though it's a low percentage, a lot of them die.
So both of them could be equal power, one because it's very viral, and the other because it's very deadly.
But what happens if you get something that's super viral, and by the worst turn of fate, super deadly?
I don't believe we've had one of those.
Maybe the Spanish flu qualifies, but I'm not even sure that one was the super deadly, super viral.
I'm not sure how that gets classified.
But in the earliest days of the pandemic, was there enough information that the experts could know if they had something that was very viral, but only a little bit deadly?
Or something that was...
A 9 out of 10 in virality and deadly.
They didn't know. They were looking at China and things looked bad, right?
So what do you do if you're the experts and you've got to advise the policymakers what to do?
Either to do nothing or do something, and you don't have any information.
Well, you're going to take your best instincts and judgment, your risk management, your not wanting to kill millions of people, and you're going to have to convince leaders who are going to ask you for your data.
What do you do?
So the president says, okay, this is important, I get it, now show me your numbers.
What do they have?
Well, not much of anything, right?
Because they just don't have good numbers, they have guesses, they have assumptions, there are lots of variables.
It's really all very complicated.
Then what does the president do?
How do you convince the president, even though you're the experts, and all you have is hunches and guesses and patterns and things from the past that were similar, You've done some math in the back of an envelope.
Can you convince the president?
And could you convince the public with something like that?
Could you go directly to the public and say, look, we can't prove it, but a whole bunch of us who are experts have a really bad feeling about this thing.
And we don't want a million people to die.
So, could they convince the public?
No, because the public would say the same thing.
Based on what? Show us your proof that this is so bad.
And they would say things like, well, there was this anecdote, and there was a story, and there's this unreliable information.
There's this pattern of something that was different.
There's this time we thought we were right, that we were wrong.
And when they're done, you just say to yourself, I'm not sure I'm convinced any of this is real.
Yet, yet we still have to make decisions.
So how do you solve this situation in the real world where it's not that classic view that's on the other side of the whiteboard where the experts know just what to do.
They look at their data.
They look at their spreadsheets.
It all adds up.
They tell the leaders. The leaders say, hey, that looks good to me.
Thank you for looking into that.
And they make their policies. In the real world, that just doesn't work that way.
Even the experts...
Don't know what to do if they don't have data, and it's the fog of war, and it's a brand new thing.
Now someday, we might understand enough about this that if we could have traveled back in time with our future knowledge, well, maybe we would handle this differently.
But even the experts didn't know what was going on.
Thank you, China, right?
So here's what you do in this situation where there's something you feel really strongly about, and you're pretty sure there's a lot of evidence, but you can't communicate it.
You just can't sell it.
And so you go to the model makers.
And here's where everybody in social media and on the news is making a fundamental misunderstanding.
And it's this.
You don't use models...
To give you the answer.
Models do not produce information.
They're a sales tool.
They're for persuasion.
How do I know that?
I know. That's the opposite of what you think, right?
You think that the experts are uncertain, so they use a model to become certain and to learn something.
That's probably what you thought, right?
Ask anybody who's ever done this for a living.
That's not what happens.
Here's what really happens.
The experts say, go make us some models because I've got to sell this thing.
I need a picture. Give me a graph with a scary looking curve.
Give me a few big numbers.
Give me a range. And make it a big range because I don't want to be wrong.
Whatever you do, make it a big range.
So the model makers do their magic and they come up with some really scary looking graphs that are really simple that they can sell to scare the public and it's compatible with the experts.
Now what would happen, hypothetically, if the people making the models came up with a prediction that would kill people if it were wrong?
What are the experts going to do?
So for example, Let's say the experts are looking at the carnage over in Wuhan, and they're saying to themselves, I know there's something bad out there.
There's definitely something bad out there.
This is not normal as far as we can tell.
We haven't confirmed it, but we're seeing a lot of stuff that just doesn't look normal, you know, spraying of the streets over there and everything.
So what happens if the model maker said, well, we've put in all the variables, we've checked all of our assumptions, and the very best model we've come up with says, It's not much of a problem.
It doesn't look like it'll be a problem over here.
So what do you feel you do if you're the expert?
Because that model would disagree with what you plainly can see.
It disagrees with your sense of risk management.
It disagrees with observation, instinct, hunch.
It just disagrees with every fiber of your being.
Are you going to say to the model maker, oh, thanks, I was worried before.
But now that you ran the model, phew, Feeling better?
No. They discard that model.
Because you can't keep the model that says it might not be a problem.
That's the one that could kill people.
And remember, job one.
Job one is don't kill anybody.
So the experts can't accept a model that is too not scary.
Because it would put the experts in the position of maybe not warning the public about whatever they're seeing in China, that doesn't look good.
So they would have to reject anything that made it look like it's not a problem.
So, let's say the experts come back and they say it could be this big range, 2 million or 100,000.
But let's say the first time they come back, they say, you know, it's 2 million, or honestly, it could be 10,000.
What if that was the first model that came back?
And the experts say, um, we've got a problem here.
If it's only 10,000, or even if 10,000 is even in the range, Americans are going to think it's not a problem.
Because we're used to having things that are a huge risk, but most of the time you're fine.
If you tell the Americans that even within that predicted range is maybe just 10,000.
It's like the common cold, basically.
How are they going to act?
They're going to act in a way that they're not taking it seriously.
And then what? And then I've killed all these people.
Because they didn't, you know, potentially.
It could be worse than we think.
So, Here's what happens.
Models are simply ways that experts can convince leaders and the public that there's something that needs to be taken seriously.
Now you're saying to me, I'm not buying this, Scott.
I'm not buying this. Everybody on TV, all the scientists, all the experts, they really sort of act like this is telling you something.
Like it's actually giving you a window into the future.
That's the whole point of this.
We're spending billions of dollars making them.
There are all these models. It's the smartest people in the world, Scott.
Did you forget you're a cartoonist who has a degree in economics and an MBA? Did you forget that?
Because the smartest people in the world, they're not talking like you're talking.
No, they're not. But here's my simple argument for why they do not glimpse the future.
It goes like this.
If you have a situation that is really fundamentally like other situations, then sometimes yes.
Let's say you're into construction and you've built several homes that are comparable.
You probably could put together a budget that says, ah, based on these last three homes I built in the same town that are about the same kind, this square footage, yeah, I'll just estimate this next house will be that.
That'd be fine. But that doesn't work with a brand new situation.
There's no way to estimate it.
And if anybody could use any complicated model to predict the future, even statistically better than guessing, They would not be making models.
They would be sitting on their trillion dollar yachts.
If anybody could do this in actuality, like actually predict the future with some statistical validity whatsoever, they'd do it for stocks.
They'd do it for knowing what startups to invest in.
They could make bets on big events.
In other words, they could bet that there'll be a tragedy or a forest fire or this or that.
If anybody could look into the fog of war, which is what was happening in the beginning of the pandemic, if anybody could look into that and build a model on a spreadsheet or with any kind of software that could actually reveal the future if anybody could look into that and build a model on a spreadsheet or with any kind of And there's no such thing as magic.
So this is me letting you peer behind the screen.
Part of the reason I know this is that I did financial projections for a living.
And I can tell you that if my model and my prediction did not give management what they felt was the right thing to do, for personal reasons, professional reasons, or the good of mankind, I had to go back and tweak it until it did.
Because we didn't know what was the right thing to do, but we knew that the model wouldn't tell us.
We knew that the variables were, the unknowns were so extreme, For example, one of the things I was trying to do is calculate whether this technology that's obsolete now but was new then called ISDN would have a big future and whether it was economical to put it into the network and sell it to people.
And there were just gigantic unknowns about where the technology would go and what would happen with costs.
And if you tweaked any one of them, you'd just get wildly different results.
It was either a great idea or a terrible idea.
It was my job to calculate that.
And so it was that and other situations where I learned that you start with the answer.
And the answer was, yeah, we're a high-tech company.
We have to be investing in high-tech stuff.
Don't bring me back some financials to say we don't do that.
Because we're going to do that.
We have to do that.
Our experience, our pattern recognition, our bias...
All of our external and internal incentives tell us we're going to invest in the new technology.
And it doesn't matter, bean counter, if you come back 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 idea because that's the business we're in.
We're in this business. We're not going to be not in that business because your spreadsheet said so.
So once you see behind the curtain...
You realize that the models are always just an expression of what the experts or the leaders want them to be, and they are sales tools.
They are not things that tell you what is true.
So, for all the people who are not quite seeing behind the curtain, you can see them debating in public.
You can see it on TV, you can see it on social media, and they say things like this.
Those models were inaccurate.
Doesn't mean anything. If you actually understand what the models are for, and what is possible, it is meaningless to say the models turned out not to be accurate.
Because they were never built to be accurate.
You can't be accurate.
In the same way the models were not built to crap bars of gold for all of us.
The reason is, it would be great if you could build a model that would crap gold Wouldn't you do it?
I mean, why not?
You're a nice person. You'd give us all a little gold with your algorithm.
But you can't really build a model that can predict the future.
It's not a thing. At the people who built the model for not doing a thing that's not a thing, which is predicting the future from unknowns.
Again, if you had a situation where it was very similar to the last one, that's different.
But in the fog of war, nobody can predict.
It's not a thing. The people who understand the least, and I would argue people who have never been around financial predicting, people who are not economists, maybe people who are not scientists, maybe journalists,
if I may say so, I think the journalists and the artists are more likely to think they don't understand what's going on because the predictions were so far off what we're actually experiencing.
And I think to them, they're saying, well, was this a conspiracy?
Was everybody stupid?
And I don't think it was a conspiracy.
And I don't think anybody involved was stupid.
And I don't think anybody involved was acting with bad intent.
I think everybody involved used the only tools they could To get a result they thought would protect people the most, and maybe with a little bias toward making sure they were not the ones who killed a million people personally.
So I think everybody had good intentions and used the tools they had, but don't be fooled into thinking that the models tell you what the future is.
That's just not a thing.
But they are good for persuasion, and you can see how well they work.
Now, having...
Having observed this situation, and I think I succeeded in at least convincing some of you that the way it works is not that models tell you something.
It's pretty much the experts tell the models what the models need to say in order to get to the next level, if you will.
You can see why some people have a problem with the climate models.
I don't want to get into that.
I just want to alert you that it's hard to form an opinion on one of these things without extending it to, well, does that apply to climate change?
And so here's what I would ask you.
The climate change models, again, would be accurate if They are similar to things that have worked before.
In other words, if we were good at modeling climates, and this was just another one, maybe we'd be good at it.
But we've never modeled a climate 80 years in advance using the tools that we're using.
We've never done it. We think we're doing it now, but there's nothing to test it against.
I would suggest that the best way to understand the climate change models is this way as well, which is that the scientists genuinely believe, based on everything they've seen, every scientific test, every paper, their pattern recognition, the people they've talked to, just the whole weight of their experience, I think, is screaming to them, we're in trouble, the planet's going to warm up.
But they don't have the ability, because it's impossible, it's not because they're bad at it, it's just impossible, to communicate all the things they know, the full weight of their knowledge, to a leader or to the public.
Because it just doesn't transmit.
It would just sound like gobbledygook, and we'd say, I don't know, I'm not even sure you know what you're talking about.
And so, the scientists do the same thing that you're seeing here, which is they go to a model.
For them, the model is really persuasion.
But the public thinks the model is actually information.
The public thinks it's an actual prediction.
Now, it might be a prediction that the temperature's going up and, you know, there's a risk.
But the actual model, we'll see.
And by the way, the model can be right if they make the range big enough.
If you make the range big enough, there's a pretty good chance it's going to fall in that range.
So that is my lesson for today.
I hope that was useful.
Was it? I would like to see your feedback.
I'll stay just for a minute to look at that.
Is Bill Gates in here?
I don't know what you're talking about.
Who wins? Big business?
Well, I don't know if anybody's winning.
Very few people are winning.
So I see more people asking about Dr.
Shiva. I'm still waiting for somebody to make a statement.
Oh good, I'm getting some good feedback here.
I'm waiting for somebody to make a statement that they think they would like my opinion on that is something that Dr.
Shiva says.
So narrow it down to me, not just like a whole topic, but like is there a statement or a fact?
Just narrow it down and I'll give you an opinion.
Post your whiteboard on Twitter.
Yeah, I'll take a picture of it as soon as we get off here.
Oh, good. Actually, the feedback is better than I thought.
I didn't know if that was going to work as well as it did.
How delightful. All right.
It doesn't seem to me that you treat climate models like this.
Should they be treated like this?
Well, I just did.
Maybe you just signed on or something.
I wouldn't watch you if I didn't like your take on things.
That's true. Are you ruining models forever?
Well, I'll tell you, models were ruined for me when I became the person who made them.
Nothing will disillusion you more than being the person who's doing that thing.
If you want to be unimpressed with something that you used to be impressed at, Try being that person.
Let me give you a concrete example.
When I became a cartoonist, I immediately set my sights on winning the top award in cartooning.
Because I thought, you know, if you're an actor, you probably daydream of getting the Academy Award, right?
That would be natural. So whatever business you're in, you probably spend a little time daydreaming about winning the Super Bowl or getting the gold medal or something like that.
So I wanted to win this top award in cartooning.
It was called the Rubin, named after Rube Goldberg.
And every year the cartoonists get together with sort of an Academy Awards, and there's a vote, and they give the top award to a cartoonist.
So I, of course, wanted that.
And years go by, something like 10 years, and I'm not even nominated, and Dilbert just takes off and gets financially successful.
So suddenly Dilbert...
It's like one of the biggest things in the country.
And then I got nominated.
And I said to myself, wait a minute.
I don't think the comic is that much better than it was a year ago, or two years ago, or three years ago.
But what is different is that events and society sort of propelled me in front of a lot of people, and then the comic took off.
So the thing that was different is I was making a lot of money and getting a lot of attention.
And I thought to myself, what kind of award for cartooning depends on how much PR I'm getting?
And so I actually ended up winning the two top awards in cartooning.
One is the overall award, you know, the biggest one you can get, and then the top award in my category, which was cartoon strips.
And so I won them both in the same year.
And, of course, I was, you know, Terribly honored and everything.
But almost as soon as I won them and got home, the entire thing seemed like a farce and didn't mean anything to me.
Because once I got on the inside, and I was the actual winner of it, I just realized it wasn't so much because of my cartooning.
It's because I had become a name brand, and the organization that puts on the event likes to have name brands, so it makes everybody else want to go.
It just makes the event better if you've got some famous people there.
So I had won the award simply as a way to get me to attend the event.
It had nothing to do with anything.
I spent years of my career lusting after this award that when I won it, I realized was completely meaningless.
If there's anybody out there who has won that award, sorry.
Sorry about that.
My college, my undergraduate college did the same thing.
When I got famous, they made me Alumni of the Year or something, and invited me to go back and give a talk and everything.
And I thought, my God, this is what an honor.
I'm the Alumni of the Year of the little college I went to.
I was like, man, of all those people, won't they envy me, my old classmates, when they see that I'm the Alumni of the Year.
And then you go back and you realize it's just a fundraising thing.
Once you get famous for doing something, you're alumni of the year so that they can have more access to you for fundraising.
Anyway, that's more than you wanted to know.
And I will talk to you in the morning.
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