Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show
Speaker 1: where we explore the stories behind the stories in the news.
Speaker 1: I'm Noah Feldman. These days, it seems we live and
Speaker 1: die by the model. You can't turn on your phone,
Speaker 1: or open the newspaper or watch the television without being
Speaker 1: hit in the face by some sort of model or
Speaker 1: graph or chart that purports to show you how fast
Speaker 1: the coronavirus is likely to spread, when it will peak,
Speaker 1: whether it will plateau, how many people will die, you
Speaker 1: name it. These graphs and charts and models are constantly changing,
Speaker 1: and sometimes they are in conflict with one another. So
Speaker 1: how should we be making sense of all of this?
Speaker 1: Here to help is Carl Bergstrup. He's a computational biologist
Speaker 1: at the University of Washington who's got a deep background
Speaker 1: both in epidemiology and in model building and in model analysis.
Speaker 1: He's also an expert on the spread of misinformation. With
Speaker 1: his co author Jevin D. West, he's written a forthcoming book,
Speaker 1: Calling Bullshit, The Art of Skepticism in a Data Driven World.
Speaker 1: I spoke to Carl on Monday afternoon. Carl, you are,
Speaker 1: by profession some combination of a modeler and an explainer
Speaker 1: of models and a debunker of bad models, and we
Speaker 1: now live in model world twenty four seven. So my
Speaker 1: first question before I ask you to do all three
Speaker 1: of those things simultaneously is how weird has this time
Speaker 1: been for you? That's a good question. I think it's
Speaker 1: hard when you're in the middle of it. You don't
Speaker 1: even really pause to think about whether it's weird or normal.
Speaker 1: It just is. The thing that strikes me as the
Speaker 1: strangest in a sense, is that I spent many years
Speaker 1: doing confectious disease epidemiology, and then, by various convoluted paths,
Speaker 1: ended up over the last few years studying the spread
Speaker 1: of misinformation on social networks. And to have those two
Speaker 1: things come together the way that they're coming together right
Speaker 1: now has been really striking. Let's start with a model
Speaker 1: that you've spoken in favor of, in fact strongly in
Speaker 1: favor of, namely the simple model that produced the expression
Speaker 1: which has now entered the lingo of flattening the curve.
Speaker 1: Say a word about why that basic curve with the
Speaker 1: flattening was so effective. Well, I think it's the notion
Speaker 1: of a very simple idea made concrete with a nice picture,
Speaker 1: and so you know, the most important thing about that
Speaker 1: picture was that it is worth a thousand words, and
Speaker 1: those thousand words are worth thousands of lives, I believe,
Speaker 1: because they showed people at the time why one argument
Speaker 1: that was floating around was not a good argument. And
Speaker 1: what had been floating around at the time was, look,
Speaker 1: we're all going to you know, people were saying, we're
Speaker 1: all going to get this or a large fraction unless
Speaker 1: they're going to get this, and there's nothing we can
Speaker 1: do about it. So let's get this over with as
Speaker 1: quick as possible. So they must talk about taking it
Speaker 1: on the chin, talk about you know, why would you
Speaker 1: prolong the economic disruption and so forth. And one thing
Speaker 1: that people hadn't been thinking about was the sort of
Speaker 1: way that if we didn't do anything to control the pandemic,
Speaker 1: then we would badly exceed hospital capacity. And so what
Speaker 1: this one little picture did was stressed that you have
Speaker 1: to worry not only about the area under the pandemic
Speaker 1: curve where the epidemic curve, which also have to worry
Speaker 1: about the height of that epidemic curve at any given
Speaker 1: time because we have a limited hospital capacity and for
Speaker 1: this particular disease, I see you care saves lives. So
Speaker 1: I just shifted the framework of people's thinking in a
Speaker 1: very simple way, and I think, you know, people definitely
Speaker 1: started rallying to this notion of flattening the curve, and
Speaker 1: people now take that for granted. It's time to start
Speaker 1: thinking about new models and new new pictures now that
Speaker 1: this is something we all take for granted. But I
Speaker 1: think certainly some of the reason we haven't exceeded healthcare
Speaker 1: capacity any worse than we have is that people kind
Speaker 1: of took this message to heart and realize that there
Speaker 1: is an important public health role that'll be played by
Speaker 1: trying to slow down the spread of this early on, Carl,
Speaker 1: I want to ask you a sort of philosophical question
Speaker 1: about models that's been really very much front of mine
Speaker 1: for me, and that is, broadly speaking, the relationship between
Speaker 1: a model that's meant to tell you what you should
Speaker 1: do in life, a kind of normative model like the
Speaker 1: model that you were talking about, the flattening the curve model,
Speaker 1: and a descriptive model, a model that's meant to describe
Speaker 1: the world as it is or as you think there's
Speaker 1: some probability of it being. This is a very hard
Speaker 1: line to draw. But I think it's a distinction that
Speaker 1: really matters because we're in this delicate moment now where
Speaker 1: people are observing that models that were meant to say, hey,
Speaker 1: if you don't do anything, you'll have results X and
Speaker 1: Y and Z are now giving way to new models
Speaker 1: that say, well, we've updated the models in light of
Speaker 1: the social distancing that we've done. And that's leading some
Speaker 1: part of the public, including even the somewhat educated public,
Speaker 1: I think, to say, well, wait a minute, maybe those
Speaker 1: initial models were grossly overstated. And it seems to me
Speaker 1: that part of the distinction here is the difference between
Speaker 1: a model that is meant to say, take action and
Speaker 1: a model that's meant to say, here's the description of
Speaker 1: the world, updating for what we have done. Could you
Speaker 1: say a word about that distinction. Yeah, I mean, it's
Speaker 1: not actually a distinction I had been thinking along about.
Speaker 1: I mean, I would have thought, you know, more about
Speaker 1: the fact that the models have a feedback in terms
Speaker 1: of the models influence our behavior, which influences the data
Speaker 1: that go into the model that you know as you
Speaker 1: go through this loop. And so what's happening, of course,
Speaker 1: is that early models, including the flat and the curve
Speaker 1: just a conceptual model influence people's behavior, which then changes
Speaker 1: the situation that we're in. And so now people can say, oh, well,
Speaker 1: you know that was stupid. Why didn't we flatten the curve.
Speaker 1: Hospitals haven't been overrun. It's like, well, yeah, hospitals haven't
Speaker 1: been overrun precisely because we locked down places and flatten
Speaker 1: the cur and that's why. You know, except for a
Speaker 1: New York City, essentially most places are managing pretty recently
Speaker 1: in terms of hospital capacity. I'd kind of, rather than
Speaker 1: saying there are models that are normative and models that
Speaker 1: are descriptive, I would say, there's the models can be
Speaker 1: used in both of those directions, and so you can
Speaker 1: come up with a model likely fight the curve model,
Speaker 1: and that sort of describes two different things that could happen,
Speaker 1: and then you have to draw your own normative conclusions
Speaker 1: from that. So, you know, we could have this big
Speaker 1: fast peak, we'd be done with it by the middle
Speaker 1: of the summer. We'd all be back, you know, those
Speaker 1: of us who are still alive, to our normal lives.
Speaker 1: But we would have had to live through this period
Speaker 1: where the hospitals are massively over capacity. We would have
Speaker 1: lost friends and relatives and neighbors and so on. Or
Speaker 1: we can try this other approach, which we don't even
Speaker 1: know exactly how we're going to manage to keep this
Speaker 1: thing down run in the future, but we can at
Speaker 1: least get ourselves onto that track so that we can
Speaker 1: solve the problem. And then if we do that, we're
Speaker 1: probably going to be looking at a more protracted period
Speaker 1: of life being different, but we're going to avoid these
Speaker 1: really catastrophic periods of exceeding health capacity. So I guess
Speaker 1: that would be sort of my distinction, which is perhaps
Speaker 1: a little different than the one you're driving. So if
Speaker 1: I understand you correctly, what you're saying is the model
Speaker 1: itself is at least arguably neutral, and then it can
Speaker 1: be used either to make a descriptive point about how
Speaker 1: the world is or a normative point. And I guess
Speaker 1: my pushback on that, if I'm understanding you correctly, would
Speaker 1: be that because as you say, you're modeling either taking
Speaker 1: into account probable effects on the world or not taking
Speaker 1: those things into account. It's tricky, maybe not impossible, but
Speaker 1: tricky to separate out a model that says, hey, this
Speaker 1: is the way the world, you know, take it or
Speaker 1: leave it. But this is the world way the world
Speaker 1: would be if you do nothing, versus here's a model
Speaker 1: based on what we think will happen if you do
Speaker 1: take these steps. Yeah, I think that's right, and I mean,
Speaker 1: but I agree with you about that, and I also
Speaker 1: with respect pushback. I mean, I pushback. I'm making you
Speaker 1: know this claim that they're absolutely neutral myself, because I mean,
Speaker 1: models or tools, models are designed for purposes, and so
Speaker 1: people are making each of these models for some particular reason.
Speaker 1: You know, the flat and the curve was made to
Speaker 1: try to help people see that it's not just the
Speaker 1: total number of cases, it's also the timing of those cases,
Speaker 1: whether they all happen at the same time or not.
Speaker 1: It had a purpose. I think you can kind you
Speaker 1: can kind of see them that way when I look
Speaker 1: at some of the models that people are doing now,
Speaker 1: For example, the there's this IGMME model that's being used
Speaker 1: a lot that predicts hospital needs by state and also
Speaker 1: death rates by state and so on, and so that's
Speaker 1: a model that's been designed to be used precisely for
Speaker 1: thinking about, you know, what kind of equipment, how many
Speaker 1: beds do we need and so on, So you know,
Speaker 1: even though you can say, well it's just a model
Speaker 1: of what happens, it's still designed to this purpose. And
Speaker 1: then once that purpose comes in, then you know that
Speaker 1: purpose is usually to make a decision, and so the
Speaker 1: whole normative aspect of modeling flows into that through that channel.
Speaker 1: I think if we're going to try to reopen the economy,
Speaker 1: cause before we have the capacity, assuming we ever get
Speaker 1: the capacity to do millions and millions of tests, then
Speaker 1: there are going to have to be, as it were,
Speaker 1: many spikes in the curve. We just want those spikes
Speaker 1: to be below the point where they overwhelm the hospitals.
Speaker 1: What's a model that sort of pictures that? I mean?
Speaker 1: Is it a kind of sign curve model where the
Speaker 1: top of the curve is just beneath the number of
Speaker 1: people who will flood the hospitals? Is that what we're
Speaker 1: realistically talking about until such time as we have either
Speaker 1: a vaccine or very extensive testing. Well, I'm not very
Speaker 1: optimistic about that approach, because you don't get something like
Speaker 1: a nice smooth signed curve, but you usually get as
Speaker 1: a pretty rapid ramp up to being at hospital capacity
Speaker 1: and a fairly slow drop off and then a rapid
Speaker 1: ramp up at a fairly slow drop off, so you
Speaker 1: actually spend most of your time with social distancing still
Speaker 1: han and only these little gaps with social distancing off.
Speaker 1: That's what came out of the Imperial College model at
Speaker 1: the scenario. And then there are been other plans along
Speaker 1: these lines, a two day work week plan that our
Speaker 1: lines groups proposed and so on, and they have this
Speaker 1: general form. So that's one problem. The other problem is
Speaker 1: you're dealing in these cases with exponential growth and imperfect measurement,
Speaker 1: and so when you try to manage exponential growth to
Speaker 1: sort of top it out right below hospital capacity, that's
Speaker 1: a very difficult call to make given the amount of
Speaker 1: information you have. And so what's going to happen is
Speaker 1: you're going to miss, and you're going to miss by
Speaker 1: a lot because it's exponential growth. So I think the
Speaker 1: other thing, you know, there was a nice paper that
Speaker 1: you looked at that sort of optimal management of a
Speaker 1: pandemic like this and keeping it under hospital capacity. But
Speaker 1: the conclusion, informally stated, you know, was essentially, don't try
Speaker 1: to get cute with epidemic growth. And I think that's
Speaker 1: kind of a key message that comes out of modeling
Speaker 1: there is. It's you know, as soon as you start
Speaker 1: thinking about the uncertainties that are in place, you recognize
Speaker 1: that actually keeping it under capacity is an almost impossible challenge.
Speaker 1: And making a really powerful point about the likelihood of
Speaker 1: best case and worst case scenarios, which I think is
Speaker 1: quirky and totally non obvious to the labors of whom
Speaker 1: I am a good example. So we often think, well,
Speaker 1: there are two models. There's a best case, they're in
Speaker 1: a worst case scenario, and in life things will probably
Speaker 1: be somewhere in the middle. That's how we often think.
Speaker 1: It's a kind of Goldilocks principle. Yeah, we go through life,
Speaker 1: and you have been arguing that that's exactly wrong. In
Speaker 1: thinking about an epidemics say more for us, Well, epidemics
Speaker 1: are an interesting kind of dynamical process. There are sort
Speaker 1: of attractors in an epidemic. You can have a dynamic
Speaker 1: where you relatively quickly suppress an epidemic, and you go
Speaker 1: to a relatively small number of cases where you can
Speaker 1: have a dynamic where it sort of blows up and
Speaker 1: racist through the population. And whether you have this former
Speaker 1: case where you suppress it or the latter case where
Speaker 1: you fail depends basically on this number or not that
Speaker 1: everyone's talking about basic reproductive numbers. So it's how many
Speaker 1: cases does an initial infected case generate downstream, And of
Speaker 1: course that number depends not only in the properties the virus,
Speaker 1: but on the amounts of social distancing we're doing and
Speaker 1: so forth. And so as we've gone into this lockdown
Speaker 1: period in the social distancing, we've tried to knock our
Speaker 1: not down pretty low, and we've got it down around one,
Speaker 1: but we don't know exactly where it is, and so
Speaker 1: if it's a little bit below one, then each case
Speaker 1: generates fewer than one additional cases, and we get this
Speaker 1: exponential fall off in the number of cases, and we
Speaker 1: might end up with say about three percent of the
Speaker 1: US having been infected by the virus after the first way.
Speaker 1: If we're just a little bit above one, then each
Speaker 1: case generates more than one new cases, and it grows
Speaker 1: and grows, and it grows exponentially, and so it races
Speaker 1: through the whole population. And if that happens, you're going
Speaker 1: to end up with you somewhere in the range of
Speaker 1: thirty to seventy percent of the population infected in the
Speaker 1: absence of continued intervention by people changing their behavior so
Speaker 1: that this are zero are not is adjusted over time,
Speaker 1: you really end up on one of these two trajectories
Speaker 1: or not. Compared to rolling a ball down a ridge
Speaker 1: or a fence, I mean, Paul may stay on the
Speaker 1: ridge line for a while, but sooner or later it's
Speaker 1: going to drop off of one side or the other
Speaker 1: just because your trajectory is just slightly in one direction
Speaker 1: or the other. And so sort of the what happens
Speaker 1: in the pandemic is a bit like that. What's really
Speaker 1: interesting is something that people have suggested to me that
Speaker 1: we may have this element of what physicists call self
Speaker 1: organized criticality where actually, even though you've got this unstable
Speaker 1: point where or not is one where you're kind of
Speaker 1: rolling down the ridge line, the pandemic starts to take
Speaker 1: off and really accelerate, Then people get scared and they
Speaker 1: do more social distancing, and they push it back down.
Speaker 1: If the pandemic starts to get shut down, then people
Speaker 1: start to relax and they do less. And so you've
Speaker 1: got on one hand, you've got the fundamental dynamics of
Speaker 1: the pandemic. If everyone's behaviors helped constant we'd either have
Speaker 1: a great, big one or we'd shut it down pretty quickly.
Speaker 1: People are modulating their behavior, and they may actually be
Speaker 1: pushing us along this along the ridge, yeah, or on
Speaker 1: the ridge exactly. So that's an interesting sort of second
Speaker 1: layer on all of this. I'm glad you raised that issue,
Speaker 1: because that's exactly what I was and to ask about it.
Speaker 1: I mean, in a dynamic picture where we're always reacting
Speaker 1: to whatever we see happening out there, you could imagine
Speaker 1: a government at least trying very hard to get its
Speaker 1: people to type trate going to work and then coming
Speaker 1: home from work, going back and forth, and then getting
Speaker 1: some of a thing in the middle. I thought, in
Speaker 1: that context of the Swedish example, I mean, Sweden is
Speaker 1: sort of functioning as a kind of experiment where they're
Speaker 1: doing a fair amount of social distancing, but in otherwise
Speaker 1: way as they aren't, it'll be really interesting to see
Speaker 1: maybe they will turn out somewhere in the middle. Yeah,
Speaker 1: I mean it's certainly possible. I think even there, you're
Speaker 1: kind of unlikely too, because not only do you have
Speaker 1: this system that's you know, with exponential growth, but then
Speaker 1: you've got long delays, so there are big delays between
Speaker 1: when people are infected and when we start to really
Speaker 1: observe the consequences of those infections. You know, with full
Speaker 1: on testing, it would be five days to a week.
Speaker 1: If we're waiting for people to end up in the hospital,
Speaker 1: we're looking at more like ten to fifteen days. And
Speaker 1: so whenever you're trying to do control on a system
Speaker 1: that has exponential growth, implification had major delay place that
Speaker 1: control becomes extremely hard and you're overshooting. You you're fish
Speaker 1: tailing and overcorrecting, and you're going to end up off
Speaker 1: the road. We'll be back in just a moment. Can
Speaker 1: I ask you to put on your misinformation science has tad?
Speaker 1: What are the most egregious examples that you've seen thus
Speaker 1: far in this pandemic of misinformation with observable real world consequences?
Speaker 1: You know. Unfortunately, I think some of the most egregious
Speaker 1: misinformation in terms of the magnitude of the consequences has
Speaker 1: been coming out of the White House. And it was
Speaker 1: the protracted period where there was a serious effort to
Speaker 1: downplay the magnitude of what was going on, and that
Speaker 1: delayed the national response in a whole bunch of ways.
Speaker 1: And I think it also has the consequence of really
Speaker 1: hemorrhaging the trust that people need in order to comply
Speaker 1: over the long term with public health measures, which are
Speaker 1: more or less our only way of controlling the pandemic
Speaker 1: right now. So, if you have a story that is
Speaker 1: changing all the time, where you've got different agencies presenting
Speaker 1: different version of the story, You're hearing from some that
Speaker 1: this is going to go away on its own, that
Speaker 1: it's on the way down, it's going to disappear in April,
Speaker 1: and whatever the case is, and then the same government
Speaker 1: has other agencies telling you this is a serious threat,
Speaker 1: there's going to be protracted to a human to human
Speaker 1: spread in the United States, etc. People start to not
Speaker 1: be sure who they can trust. And when that happens,
Speaker 1: then it becomes harder and harder to manifest the political
Speaker 1: will that you need to take unpopular measures like closing schools,
Speaker 1: closing non essential businesses and like so. I think, but
Speaker 1: in terms of like the measurable consequences, one of the
Speaker 1: worst things that's happened in terms of misinformation has been
Speaker 1: this initial attempt to downplay the seriousness of a pandemic
Speaker 1: in order to I think, prop up the stock market
Speaker 1: on a first or you have a book coming out
Speaker 1: soon with the wonderful title calling Bullshit. Presumably when you
Speaker 1: were writing the book was before the pandemic broke out.
Speaker 1: What were the things that at the time you wanted
Speaker 1: to call bullshit on? And then maybe from there we'll
Speaker 1: go to how that's relevant in the present moment. So
Speaker 1: when we wrote the book, we were really thinking about
Speaker 1: the ways that quantitative information is used to mislead people.
Speaker 1: So what we thought was very, very important was to
Speaker 1: teach people that you are not at the mercy of
Speaker 1: the person who's bringing data and statistical analyses and machine
Speaker 1: learning or whatever it is to the table. You don't
Speaker 1: have to just accept those because you are not a
Speaker 1: PhD satistician, or you're not a computer scientist or whatever.
Speaker 1: The basic way that we think about this is that
Speaker 1: with any of these systems you've got, you know, when
Speaker 1: people are drawing conclusions based on data, they collect a
Speaker 1: bunch of data. That data then or put into some
Speaker 1: kind of machinery which you can think of as a
Speaker 1: black box out the other end, and comes some results,
Speaker 1: and then people draw conclusions from the results, and the
Speaker 1: bullshit is rarely in the black box where we find,
Speaker 1: so the bullshit is rarely in hard effect of the
Speaker 1: technical construction of the model. Of course, there are cases
Speaker 1: where that is what happens, but that's a strong minority
Speaker 1: of the bullshit that we see out there. Almost all
Speaker 1: of it is because people have data that are not
Speaker 1: necessarily representative or appropriate for the question that they're asking.
Speaker 1: You're comparing apples and oranges. They've got a biased sample.
Speaker 1: There are all these kinds of things that can go wrong,
Speaker 1: or people get results out and then they draw unjustified
Speaker 1: conclusions from the results. They overgeneralize, they infer causality where
Speaker 1: they've only got an observational study and only know about correlation,
Speaker 1: these kinds of things. And so what we wanted to
Speaker 1: really stress with the book was that you don't have
Speaker 1: to be sort of held hostage by people that have
Speaker 1: the numbers, because you can use the basic critical thinking
Speaker 1: skills that anybody has in order to see through this
Speaker 1: sort of stuff. You say, well, these data appropriate for
Speaker 1: answering that question, to these conclusions actually follow from those results.
Speaker 1: I love the idea that careful critical thinking and analyzing
Speaker 1: the premises can really help you identify bullshit when it
Speaker 1: is being dealt to you. I want to ask you
Speaker 1: though about a variant on that which I think may
Speaker 1: be pretty different that we've been seeing throughout the current pandemic,
Speaker 1: which is that it's not just that non experts in
Speaker 1: fields are calling bullshit on models, they also think that
Speaker 1: they can build their own models better. And at first
Speaker 1: I thought to myself, this problem is mostly out there
Speaker 1: on medium, or it's you know, the co worker in
Speaker 1: the office. And I actually wrote a column for Bloomberg
Speaker 1: where I write a column saying, well, the amateur epidemiologists
Speaker 1: please just sit down and shut up. So this phenomenon
Speaker 1: seems to be kind of everywhere right now, and I'm
Speaker 1: wondering if there's, like, is there a cognitive thing going
Speaker 1: on here? If people think they can find a problem,
Speaker 1: which is true by your view, they also think that
Speaker 1: they can then devise a better model, and at least
Speaker 1: as far as I can tell, that's not true. They
Speaker 1: usually cannot devise a better model. Well, I think, you know,
Speaker 1: on one hand, we only need input from a lot
Speaker 1: of sources, and so I think that it's really important
Speaker 1: to recognize that there are going to be good ideas
Speaker 1: coming from outside of the epidemiology community at the same time,
Speaker 1: one wants to be aware of the dunning Krueger effect.
Speaker 1: You know, the basic idea of the dunning Kruger effect
Speaker 1: is you don't know enough to know you're wrong, essentially,
Speaker 1: and so you think that you have a very good understanding.
Speaker 1: And so the Dunning Kruger effect typically has this sort
Speaker 1: of you know, non monotone distribution of people's confidences. If
Speaker 1: they don't know much, they're quite confident. If they go
Speaker 1: some they aren't confident, and if they know enough they
Speaker 1: start to get relatively confident again. And so we're seeing
Speaker 1: a lot from both of those confident peaks. Professional epidemiologists
Speaker 1: and otherwise there are some really good ideas coming from
Speaker 1: people that are not professional epidemiologists. But I think one
Speaker 1: of the things that's hard is when things are presented
Speaker 1: as a fact instead of suggestion, and particularly in a
Speaker 1: context where these stupid epidemiologists they don't even know about
Speaker 1: such and such. So this is the sort of the
Speaker 1: not helpful side of things. I'm personally collaborating with a
Speaker 1: number of people who are outside of the epidemiology community,
Speaker 1: not only economists on the economics side, but you know,
Speaker 1: for example, been consulting with a group of baseball analytics
Speaker 1: people that wanted to find a small, tractable problem that
Speaker 1: they could contribute to because they're really really good at
Speaker 1: figuring out things from numbers. And you know, they called
Speaker 1: me and were they understood that they needed some background
Speaker 1: in order to be able to do something useful. They
Speaker 1: couldn't just sit down and make their own model, and
Speaker 1: so we spent a lot of time talking about what's
Speaker 1: sort of an unsolved problem that they could take a
Speaker 1: crack act. And you know, I think that kind of
Speaker 1: thing is really constructive, So I guess, yeah, it's a
Speaker 1: it's a double its sort. Well, if you're helping saber
Speaker 1: metrics to save the world, that'll be a further contribution
Speaker 1: that you're making. Yeah, well it's kind it's kind of fun.
Speaker 1: You know, we're trying to figure out how to epidemologists
Speaker 1: are trying to figure out how to save baseball, and
Speaker 1: these guys are trying to figure out how to do effidemiology.
Speaker 1: So it's a nice combination a mutual assistance society in
Speaker 1: a moment when we very much need that. Yeah, Carl,
Speaker 1: what am I not asking you that you think I
Speaker 1: should be asking you? What? Are what are salient problems
Speaker 1: on any of your dimensions of expertise that you're observing
Speaker 1: that you think people should know about. I think at
Speaker 1: this point, the really big salient problem is to think
Speaker 1: about how do we emerge from the situation that we're
Speaker 1: in now. We all need to do that. We can't
Speaker 1: wait around for twelve to eighteen months, and so what
Speaker 1: are the possible solutions. And there's so many different disciplines
Speaker 1: that are involved in finding these solutions. You know, how
Speaker 1: do we need to restructure the economic system so that
Speaker 1: we can in healthy ways help businesses weather this shutdown?
Speaker 1: As you're asking those questions, at the same time, you're
Speaker 1: trying to figure out things, you know that are really
Speaker 1: technical immunology problems, like how long does immunity last, what
Speaker 1: fraction of people generate, is it even safe for people
Speaker 1: who've had the disease go back to work, etc. So
Speaker 1: someone out we need to find very good ways for
Speaker 1: us to as an extremely broad community to sit down
Speaker 1: and talk across these boundaries without epidemiologists saying that economists
Speaker 1: don't know anything about the economy or vice versa. So
Speaker 1: that's really important finding that way to communicate. I think
Speaker 1: you know. The other you know, one of the other
Speaker 1: things that has been really pressing challenge that none of
Speaker 1: us saw coming really was we've you know, we've been
Speaker 1: planning for a crisis like this for a long time
Speaker 1: and thinking about what it would look like and how
Speaker 1: we might react, and hoping it would never come. In
Speaker 1: the epidemology community we have and now we're here, of course,
Speaker 1: and the thing we weren't really thinking about was about
Speaker 1: the way that all of this was going to be
Speaker 1: so heavily politicized that we feel like we're fighting a
Speaker 1: battle on two fronts. We're fighting a battle against the virus,
Speaker 1: but then we're fighting a battle against misinformation around the
Speaker 1: virus that's being promulgated by people up to it, including
Speaker 1: the White House. And so that's a challenge and threat
Speaker 1: that we just hadn't been thinking about seriously. And I
Speaker 1: think that was a missed opportunity on our part to
Speaker 1: not be thinking about the information side of this. Do
Speaker 1: you think that's partly the result of a kind of
Speaker 1: unexpressed but mistaken sense of cultural superiority. So I know,
Speaker 1: epidemiologists who have worked extensively in subserent African Ani Bola
Speaker 1: for example, and who were acutely attuned and have written
Speaker 1: extensively about how do you deal with public misinformation, but
Speaker 1: also with governmental distortions, you know, regimes that aren't willing
Speaker 1: to play ball, or the distort facts and circumstances under
Speaker 1: some conditions. And that was I think pretty commonplace in
Speaker 1: the epidemiological community, in the public health community as well.
Speaker 1: But there were somehow this unspoken thought that that can't
Speaker 1: happen here, not that a pandemic can't happen here, but
Speaker 1: that in the United States or in some Western European country,
Speaker 1: we would all have Angelo mercles, you know, we would
Speaker 1: have rational home and political leadership. And of course that's
Speaker 1: totally false, and it was knowably false. I would have
Speaker 1: said in advance, I think that's a very sharp observation,
Speaker 1: and I completely agreed. At least I fell into that trap.
Speaker 1: I don't want to speak for everybody. You're completely right
Speaker 1: that there are people working in other parts of the
Speaker 1: world that are well aware of these challenges, and I
Speaker 1: just don't think we were thinking as seriously about it
Speaker 1: as we should have been. You know, Obama was in
Speaker 1: some ways, in a lot of ways close to that
Speaker 1: morbial mold and we weren't really thinking about that. I mean,
Speaker 1: I remember going back to Bush administration and thinking, you know,
Speaker 1: in the discussions with the Bush administration, there were concerns
Speaker 1: about the degree to which the government should step in
Speaker 1: and be involved in something like pandemic planning. Why can't
Speaker 1: it be privatized and so on. So we had these
Speaker 1: sort of ideological disagreements about the best way to handle
Speaker 1: something like a pandemic planning, But we very much had
Speaker 1: the sense that if the thing actually broke out, we'd
Speaker 1: all be on the same page, and we'd all acknowledge
Speaker 1: that it was happening and we try to do the
Speaker 1: best we could to get rid of it, and there
Speaker 1: wouldn't be this period where we were pretending that it
Speaker 1: wasn't happening at all. Carl, thank you for an extremely
Speaker 1: illuminating conversation and for your work, and please keep up
Speaker 1: bullshit on the things that need bullshit called and clarifying
Speaker 1: things for us. I really appreciate your time. Thanks. So
Speaker 1: I'm talking talking to Carl really brought home to me
Speaker 1: just how dependent we are on models from making sense
Speaker 1: of what's going on here. It's not only that they're everywhere,
Speaker 1: it's that they have the capacity to fundamentally shape our
Speaker 1: ideas about what we ought to do. Indeed, the very
Speaker 1: phrase flatten the curve, which has been a kind of
Speaker 1: motto for all of us in this early period of
Speaker 1: the pandemic, is itself language directly taken from modeling, and
Speaker 1: I can't think of the last time that a modeling
Speaker 1: term became our guide for how we should be behaving
Speaker 1: at the most fundamental level of our ordinary lives. Yet
Speaker 1: at the same time that Carl shows us the importance
Speaker 1: of models, he's also very attuned to the idea that
Speaker 1: models can be deceptive, and indeed that they can lie.
Speaker 1: And they can lie, he says, if we fail to
Speaker 1: take into account using our critical faculties, what's going into them.
Speaker 1: The problem, he says, is not that the models themselves
Speaker 1: are fallacious, that if the premises are wrong, we can
Speaker 1: be led to very, very bad conclusions. Carl is also
Speaker 1: closely focused on the question that we've been thinking about
Speaker 1: here at deep background, and then all of us are
Speaker 1: going to continue thinking about going forward, namely, how do
Speaker 1: we come out from behind our social distancing and slowly
Speaker 1: and carefully begin the process of reopening the economy. I
Speaker 1: promise you We'll be talking more about that in the
Speaker 1: episodes ahead. Until the next time I speak to you,
Speaker 1: be careful, be safe, and be well. Deep Background is
Speaker 1: brought to you by Pushkin Industries. Our producer is Lydia
Speaker 1: gene Coott, with research help from zooe Wynn. Mastering is
Speaker 1: by Jason Gambrell and Martin Gonzalez. Our showrunner is Sophie mcibbon.
Speaker 1: Our theme music is composed by Luis GERA special thanks
Speaker 1: to the Pushkin Brass, Malcolm Gladwell, Jacob Weisberg, and Mia Loebell.
Speaker 1: I'm Noah Feldman. I also write a regular column for
Speaker 1: Bloomberg Opinion, which you can find at Bloomberg dot com
Speaker 1: slash Feldman. To discover bloom its original slate of podcasts,
Speaker 1: go to Bloomberg dot com slash Podcasts. You can follow
Speaker 1: me on Twitter at Noah R. Feldman. This is Deep Background.