The coronavirus pandemic is in one sense a kind of prism: it reveals the many interlocking systems that, until disrupted, formed the mostly invisible backdrop of modern life, challenging the economy and our models of the world at the same time that it threatens individual and social health. The virus acts on, and invites new understanding through, the complexity we only take for granted at our peril.
In SFI’s new essay series on the crisis, Transmission, our international community of scientists explores a spectrum of perspectives on COVID-19 to share a myriad of complex systems insights on our unprecedented situation. In this special supplementary mini-series with SFI President David Krakauer, we discuss and find the links between these articles—on everything from evolutionary theory to economics, epistemology to epidemiology—to trace the patterns of a deeper order that, until this year, was largely hidden in plain sight.
Read the Transmission series articles we discuss in this episode:
000: David Krakauer on Citizen-Based Medicine
001: David Kinney on Why Scientists Must Make Value Judgments in a Complex Crisis
002: Luu Hoang Duc and Jürgen Jost on Making the Most of Bad Data
003: John Harte on Reducing Conflicting Advice on Allowable Group Size
004: Simon DeDeo on Thinking out of Equilibrium
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Michael: David, it is a pleasure to have you back on Complexity Podcast.
David: It's wonderful to be with you.
Michael: Together apart. So we're here to talk about this new essay series that Santa Fe Institute is doing, the Transmission series, a set of complex systems perspectives on the COVID-19 pandemic, and its sort of broader consequences and implications. Why don't we start by just explaining a little bit about the villain of this drama, the virus itself. Tell us a little bit about RNA viruses, and then what you had in mind for framing this essay series as an exploration of these broader issues.
David: Yeah, absolutely. These are trying times for all of us. And I think we want to just try and get as many useful ideas on the table as possible. I thought I might just start by explaining what a virus is and what a coronavirus is, in particular, and what makes it so frightening. Viruses are parasites, and they're parasites that can only reproduce by gaining access to the cells of their hosts. And once in there, they hijack or hack the machinery of the cell, in order to copy themselves and continue their transmission.
Now, viruses come in a lot of different shapes and sizes. For some of them, DNA is their genome, like us, and some of them have RNA genomes, like the coronavirus. The coronavirus is a very big RNA virus by the standards of RNA viruses, about 30,000 bases long. The good news about that is that, unlike most RNA viruses, it's not very mutable. The reason why it's so big is to ensure that its replication is high fidelity. So most viruses that are small make tons of mistakes when they copy themselves, but the Coronavirus is very large and has a machinery in place to ensure that's not true. And it's good news for us because it means that once we have a vaccine, we won't have to constantly be reinventing it because the virus will be the same one, we hope, unlike flu, which is very mutable.
Coronaviruses have been around for a long time. They account for about 30% of common colds, along with other viruses like rhinoviruses, and usually we haven't been that concerned about them because they're not very virulent. They don't cause lots of pathology. And this one does. Now this virus is particularly horrible because it hijacks a physiological mechanism that we are all dependent on, and that's sometimes called the renin–angiotensin pathway, and this is the way that the body regulates various cardiovascular functions. The Coronavirus, this particular one, COVID-19, binds to one of the crucial cell surface receptors that we need to regulate our cardiovascular system and gains entry into the cell.
A variety of different groups are more or less susceptible to the virus; diabetics, for example, because they have more of this receptor, so there's more for the virus to access and enter, and of course, people who are potentially having medication for cardiovascular disease might also suffer because that also interferes with this pathway. So this is a particularly horrible virus because it's targeting something that we absolutely need and have and exploiting that need in order to ensure that it replicates itself effectively.
The virus itself has lots of different proteins in it, everyone's heard of the spike protein, the S protein, and this is that one that gives it its name coronavirus because they look like little sunspots, and it needs that to get into the cell. So current vaccine development is, in part, targeting that spike protein to prevent it getting in the cell. But the most frightening protein it has is called the E protein. This sits in its membrane, and there elicits an immune response. It generates what are called cytokines, which are small little peptides that lead to inflammation, little proteins that tell immune system to trigger the inflammation response. It's the inflammation response that gives rise to the edema, the filling up the lungs with water, that give us our respiratory complications. So there is this little protein, it causes our immune system to go a bit haywire, leading to these horrible pathologies that we're all hearing about. So it's not the spike protein that's necessarily causing the damage, but it's the spike protein that gives it access to the cell.
Michael: So there we have the map of the problematic in miniature. Let's move on to this essay series and the broader motivation for doing this.
David: Yeah. So, SFI has been working on virus evolution, immunology and epidemiology for 25 years, and many of our researchers are on the front line of this disease. They're forecasting the progress of disease, providing that to the CDC and the White House. They're working on tests, repurposing fundamental research labs in order to make them useful to society. And that's actually something very interesting, Michael, that all of these labs that were doing what we would call basic science or fundamental science, it's taken almost no time in this period of great urgency for them to turn around and actually provide a service to society, which I think is a really interesting fact. It hasn't really been noted that these distinctions that we often make between basic and applied or fundamental aren't really real, because when it comes to crisis and when it comes to real need, scientists are very prepared, fundamental scientists, to turn around and turn that into an applied project very quickly. So that's been a big part of what we've been up to as a community.
But beyond that, there are all these questions, the other ramifying cascading effects of this infection on behavior, on commerce, on transport, on ecology, human psychology, institutional credibility. These are the things that are making life so trying, above and beyond the primary infection; flu doesn't do this. And this series is an effort to explore why it is that a tiny bug that's thousands of times smaller than a grain of salt, that has none of the alarming features of an AI that we've been talking about kill switches for the last several years, is actually bringing down the complex systems of the world. And we would like to understand that, in order to forestall this happening ever again; what we need to put in place, what new ideas we need, what new models we need, what new social norms we should cultivate, in order to ensure this kind of tragedy doesn't repeat itself, and that's what this series will be about. So everything, if you like, all the layers of the onion, above and beyond the primary biology of the virus and epidemiology.
Michael: Before we started recording, you and I kind of mapped out how the first five essays in this series are linked to one another conceptually. I think we're going to save Simon DeDeo's for the end, but I think it's worth noting, just to connect to what you just said and to lead into your introductory piece, that this is one of those issues where we don't notice the systems upon which we depend until they break. So that very, very, very small virus, leading to a global systemic restructuring is really a perfect ... in the way that it exploits our own physiological vulnerabilities to great effect. It's also an interesting sort of invitation to a broader menu of really important fundamental complex systems concepts. So the first concept that seems worth discussing is the name of the series itself: Transmission. So why don't you kick us off with a course outline of your article that we'll link to in the show notes?
David: Yeah. What I tried to do is try to understand why it is that this particular outbreak has been so disruptive of so many complex systems. If you look at diseases like cancer or Alzheimer’s, or respiratory infection in general, or cardiovascular disease, these things have been around for a long time, are terrifying, cause many many deaths, and there's very little prospect of us curing them in the next decade or more. And yet, they haven't led to catastrophic failure of systems. And I wanted to understand why.
Now, one of the characteristics of these diseases is that they're truly complex in the following sense: If you look at cancer, there is no one gene that is responsible for the disease, there are many tens, if not hundreds, if not thousands. And that's made treatment very hard, because you can't target one site in the genome. So that's a negative from the point of view of intervention. On the other hand, that is a property of complex systems that's critical for robustness. Our body doesn't want to give one lever or one switch that controls the entire system to one function or one disease because it collapses the complexity into simplicity, and it allows for the possibility of complete disruption with a single point of change. So complexity is actually what we would call homeostatic. It's a good thing, it's hard to understand, but it means is much harder to hijack.
Now, look at this particular outbreak. What was very clear to me and many others is that there was one basic dominant causal principle at all complex scales, and that's transmission. So the virus transmits from cell to cell, it then transmits from body to body in social networks, it then transmits across cities, through commerce, and surfaces. It then transmits from city to city through transport networks, and then across the globe. And it hijacks supply chains, in some sense, in the same way that it was hijacking the ACE receptor. It's hijacking the transmission mechanisms of our world. And that does something strange. It leads to a paradoxical simplicity, which gives the virus huge power over a number of scales that wouldn't be true, for example, for Alzheimer's. That was the first point I wanted to make, which is: human culture has become vulnerable at the layer of transmission because too many different mechanisms are aligned. So moving forward, we have to think about engineering misalignments, in some sense, such that this can't happen.
The second point I was trying to make is: quarantine and social isolation are actually collaboration in this world; by staying home, you're not just hiding from the menace, you're actively participating in misaligning one element of this complex system, so as to interfere with the progress of the infection. So this idea somehow that a citizen could actually play a part that was as important as a vaccine, but instead of preventing transmission of the virus into another cell at the ACE receptor level, it's preventing transmission of the virus at the social network level. So we're actually adopting a kind of behavioral vaccine policy, by voluntarily or otherwise self-isolating. I think it's a very important point for everyone to understand, and I actually argued in that article that everyone should be awarded some fraction of the Nobel Prize in Medicine for the sacrifices they're making in order to minimize the transmission.
Michael: Yeah. I think that the price would be significantly less than proposed federal bailouts, but the fractional prestige would be a nice consolation. This piece that seems linked to something that we've already discussed a couple of times on the show in the last few weeks, talking to SFI network epidemiologists, about how there's another layer of transmission, which is the informational and behavioral contagions that you allude to here, in terms of how do we get people to adopt different behavioral regimes that are going to relate to the transmission of this virus in complex ways.
David: Yeah, I know what you’re getting at. One of the things that we've been working on, our community, for the last several decades, is the mathematics of infectivity. We can write down equations or computational models to describe how things transmit. And one of the things that we discovered very early is that the same kinds of mathematics used to describe the transmission of disease could be used to describe the transmission of ideas, and the same kinds of networks that promote or impede the transmission of disease promote or impede the transmission of ideas. So that's in part what I mean by the alignment.
One of the things that happened in this particular outbreak is the memetic transmission, if you like, the transmission of ideas, was much faster than the transmission of the virus. But the disappointment, I think, that many of us felt is we didn't act on that. We weren’t, if you like, leveraging the comparative advantage of idea infection over biological infection. But that common mathematical structure that applies to both cases and more, incidentally, is very valuable because it does give us insights into how we might be able to bring these systems out of alignment to potentially control them.
Michael: Yeah. So with that we can frame the next four articles in this series as the beginning of an effort in a kind of informational contagion. Like a conceptual vaccine maybe.
Michael: Let's start with David Kinney's piece because I think this piece addresses something that's really critical here, something that we talked about when he was on the show a few weeks ago about the difference between the way that science is actually practiced and the way that it's publicly perceived, and that typically there's rhetoric around science that this is objective and purely quantitative: “Just give me the numbers.” But especially when you're dealing with models of complex scenarios that produce a spectrum of possible futures, that there is a hidden value judgment that scientists and scientific advisors have to make in communicating their findings, and that that value judgment is driven by a kind of market dynamics about the estimation of risk in giving the broadest available spectrum of possibility, versus giving the clearest and most actionable advice.
David: Yeah. As you know, Michael, here we are in New Mexico, where Los Alamos is based, where the Manhattan Project was conducted. And to my mind, that's one of the most salient examples of this fuzzy boundary line between basic science and policy. If you remember, General Leslie Groves appointed Robert Oppenheimer to basically oversee the production of the atom bomb. That first collaboration worked out, from their point of view, reasonably well. Oppenheimer was overseeing the science, and Groves was interfacing with government and society in a certain way.
But when it came to the hydrogen bomb, everything changed, and Oppenheimer was so demoralized by what had happened with the dropping of the two atom bombs, that he felt compelled to make policy remarks. And that led, as we all know, to various trials and conflicts between Teller and Oppenheimer, where Oppenheimer was accused of being a communist spy. So it's an extremely difficult problem, and scientists are, quite naturally, as a consequence, very cautious about making policy recommendations.
What David says in his transmission article is that in the late 50s there were philosophers and ethicists who were advocating for a strict division of power, and this is largely the work of Richard Jaffe and Isaac Levy. They suggest that it was the role of the scientist to just give raw numbers to policymakers and politicians and allow them to make the decisions. What David points out is: what policymakers want is certainty. So if I, as a scientist, provided you with probabilities for certain outcomes, if those probabilities are near one or zero, you will act on them. That's what you want, certainty. On the other hand, they're much less likely to be correct.
On the other hand, if I give you nuances, subtlety, and say, "Well, maybe there's a 0.6 probability of that and 0.4 four of that," you, as a policymaker, are very dissatisfied, but it's more likely to be closer to the true distribution of outcomes. So there's a catch-22 here. Scientists want to present the true numbers, but the true numbers are unlikely to be popular to the policymakers. So we are forced to exaggerate the severity of outcomes, in order to have them adopted. And I think this is actually a problem that can't be escaped.
Michael: Yeah, and in a way, this is also a sort of outbreak of its own, at least conceptually, where we're no longer assigning the accountability or the authority of these values-laden decisions to… It's sort of the typical process to want to pass the buck: blame the politician, "Well, they were just acting on the advisor." And then, it's like, "Well, actually, we have bad data…”
David: Yeah. Let me say one more point on this, actually, Michael, which I think is very important, and that is: good scientists are comfortable with uncertainty, bad ones are not. This leads to a genuine conundrum, and it's why pseudoscience flourishes in periods of uncertainty in culture. Because if the real scientist is saying, "Look, we don't have the data to make the decision. To the best of my ability, these are the odds," and a bad scientist comes along and says, "No, I know exactly what's going on.” That's why bad scientific ideas are adopted so readily and with such alacrity in periods of great stress, because they appear to give certainty in periods of uncertainty.
It's something that the public should be very cautious of. So when someone says. "I have a solution, I have a remedy," what they're really doing is exploiting this need for certainty, and I think it's a very desperately difficult situation to be in. But just like sport, where outcomes are not deterministic, the same is true of all complex systems, including disease. We have to cultivate our patience, I think, and our understanding that certainty is acquired very slowly, and be extremely suspicious of anyone who claims to have found a solution with inadequate data.
Michael: When we had Rajiv Sethi on the show and he was talking about stereotypes within the context of criminal justice, one of the things that really lingered with me after that discussion was how so much of this comes down to the ways that our brains are tuned to reduce uncertainty by grouping what are determined to be equivalent experiences, equivalent phenomena.
Now, given enough time, the ominous stranger in a dark alley, you might be able to sit down and have dinner with them and get to know them and develop a model of that person as a unique individual. But one of the problems with stereotyping and criminal justice, and also now as you just mentioned, the desire to cleave to oversimplified explanations, explanations that in a longer timeframe are going to appear maladaptive…is that we don't have the time to collect the data that we need and form the truly rigorous and robust models because of various problems with disease surveillance or economic urgency. So there's a balance that we have to strike, and I think that that leads into the next paper by Luu Hoang Duc and Jürgen Jost on what do we do when we're left with only bad data and relatively little time to act on it.
David: Yeah. So, once again, let me just clarify, in periods of uncertainty, we have to learn to live with it and try as best as we can to avoid those claiming to have certainty in the face of inadequate data. So what Jürgen and Luu Hoang have done is they make a very interesting point, and this is very paradoxical, I think. And that is that when data is really bad, you should use the simplest model at hand. When data is very good, you can use complicated models. There's been a lot of conversation about this early prognostication that came out of the Imperial College model. It had, in retrospect, potentially wildly overestimated the number of fatalities, and now, of course, that model has been modified, so as to reduce that number.
One of the problems with that model philosophically is it was vastly too complicated given the data that we had. There was a temptation, because of policy in fact, to put everything in, so we're going to put in the number of schools, the age distribution, the household, the number of hospitals, their spatial locations, the position of airports. These are these very complicated agent-based models, and those models are absolutely critical when you have really good data, like those of cities under normal conditions.
But what Jürgen and Luu Hoang are saying is, what if you don't? Well now, you should do the paradoxically opposite, use a simplest model you possibly can because they're much less sensitive to fluctuations in the data, what we would call they don't over-fit the data. The last thing you want to do is over-fit, parameterize a model based on sparse or bad data. And the particular idealization that they advocate is a linearization of the data. So if one reads that paper, you'll see they do linear extrapolations based on a logarithmic transformation—they essentially linearize the data, and they try to say that this would be, given our current state of uncertainty, the best course of action.
Now, whether that's true or not, remains to be seen, but from the point of view of us as a community and these listeners, the key philosophical point is that if someone comes to a complicated model with bad data, you should be very suspicious of it, and you should be more tolerant of simplicity in times of uncertainty.
Michael: Let me see if this analogy holds with you: we’re approaching Earth and we don't have a map of the coastlines of the continents, we're operating with a very low resolution camera deciding where to land. So you don't try to come up with a map of the coastlines based on data that's too granular, that you don't actually possess, that you basically just aim for the center of the continents and continue to update your maps as you get closer to it, as things come into focus.
David: It's reasonable, I think. Maybe a better example would be in an economic setting where we have basic laws of supply and demand. They're very, very simple models. If the price goes down, then you're more likely to purchase. If the interest rates go down, you're more likely to borrow. So these are very simple models. And you can imagine a very complicated model that said, "I know exactly what Michael likes, I know what foodstuffs he purchases, I know what TV show you like, and I’m going to put all of them into my model.” But now imagine that data was all totally bogus. That model would do much less well than the simple macroeconomic model that just looks at supply and demand regularities. So you only want to put complexity in your model, if it's justified by the empirical data. If it's not, leave it out because it will underperform the simple model.
Michael: We have an example here. John Harte gave a simple model to reduce conflicting advice on the allowable group size, as we're going through school and business closures. This is something that I brought up with Laurent Hébert-Dufresne, when we had him on the show, this question of where are we getting these numbers that say 200 people is too many, but 25 is acceptable? This sort of gets back to that David Kinney remark on having to sort of place a bet on where you draw the line. But John's got a model here that's simple enough to understand and doesn't rely on complicated extrapolations about the internal structure of these different kinds of groups, and so on. You want to talk about that a little bit?
David: Yeah. So one of the questions that we, all of us... Michael and I, like everyone else in the world, just being sensible now, are isolating and in quarantine. What we're doing essentially is reducing our exposure to others, reducing our group size. And what John asked was, "Look, what is a sensible, allowable number of individuals with whom you can interact?" And as we come out of quarantine, what the last thing we want to do is resume normal behavior where we titrate it or iterate up slowly towards normalcy.
He gives a very interesting simple analysis. He has a model: you have a number of groups that could be schools, for example. Each school has a number of students in it. Typically you have more students in each school than you have schools. And what he shows is that if you double the size of a group, you have a four-fold increase in the transmission of the disease; this is called a superlinear scaling. In other words, even small groups can have very, very high transmission rates. So when we talk about these numbers, five, 10, 100, they're actually quite meaningful. I mean, I heard early quotes coming from, I won't name who, where they said "Well, there's no real difference between 10 and 100." There's a huge difference between 10 and 100! And the sensible thing for people to do would be to be in the single digits, that's what a sensible group size is. Because of the superlinearity of the scaling, a small increase in a group leads to a very large increase in transmission.
So I would hope that what comes out of those simple models, if people can internalize this message, is that, as we basically break free of the grip of this horrible virus, we actually don't rapidly or too precipitously return to normal, but we gradually return to normal. That would be the sensible thing to do, it would be the considerate thing to do for all the citizens of the world, and I think John's model makes that extremely clear. So there is a difference between 10 and 100. There's a difference between one and 10, there's a difference betweem 10 and 20. In some sense, add one or two at a time, that seems kind of preposterous at one level, but it would be the sensible thing to do.
Michael: To link this back to your piece, this is the continued citizen science experiment, that every time anybody leaves their house, they're increasing the size of that group, and then providing data to someone, hopefully not at great personal expense…
In the case of this novel coronavirus what we have is a situation where, as people like SFI External Professor Lauren Ancel Meyers have shown in their research, the interval between someone receiving it and actually developing symptoms is great enough, this is part of why our data is so patchy on this particular outbreak, because it's probably impossible to reconstruct the actual transmission network. There are so many sort of mysterious links too far separated in time and space.
And yet we've been relatively effective, at least by some measures, at containing it. Like you said earlier, this was a very rapid and emergent social mobilization. People were isolating themselves before they were ordered to do so by the governments. At least in that respect, it's, like you said, “an antiviral flash anti-mob.” Kudos to the Internet and the citizens of the Web for being able to coordinate our misalignments.
David: I think that's absolutely right. I think you made a very good point earlier that this gets back to that notion of citizen based medicine. So, we're very used to these interventions that are in some sense biochemical, and they give us a sense of security because they're basically reductionist. But we're moving into a world now where everyone should see themselves as a part of a solution. And in a complex system, you are absolutely as important, if not more important, than the ACE receptor.
This graduated return to normalcy is actually quite a sophisticated mathematical principle. If people could acquire these new kinds of habits, we'd make the world a much better place looking forward. What we would like to do, I think, is develop guidelines for sensible forms of behavior that don't feel oppressive, but feel empowering, that give people a sense of what they might be able to do under these circumstances. And they're not easy. They're actually, in some sense, more difficult than developing a vaccine because we're developing a social analogue. So you're absolutely right. I think every citizen now is actually, in some sense, empowered to control this pandemic. And what John's article is telling us is giving us an insight into how that should be engineered. So what I would like everyone to do is develop a kind of sophisticated sense of how social networks and transmissions work, in order that they can make the best decisions for themselves and society, in terms of how we roll back to normalcy.
Michael: Right. But that brings us to Simon's piece because whatever normalcy we roll back into is going to look very different from the normalcy that was disrupted at the beginning of this year. As Andy pointed out in our episode, we've done such an excellent job at containing this, but very few of the models make it look like this is a one-wave infection. Very few of them make it look like we're going to have completely trounced this thing.
It seems very likely that there will be additional waves, that there will be additional injunctions to get go back home after we have returned to work, to distance ourselves again, because we will not have infected enough people through our successful early containment efforts to have developed a herd immunity for this. This leads us into Simon's piece: we're in a kind of interregnum here where we don't really know what the new normal is going to be. Why don't you tell us how you read Simon's piece?
David: Yeah. So this was Simon DeDeo's contribution. It's really about the dangers of habitual thinking and in relation to all that we've discussed before. Society has evolved over thousands of years, so as to minimize the cognitive burden on individuals. And we call that minimization habit formation. So we have rules of thumb, heuristics, that allow us to solve problems quickly because the environment in which we live is more or less constant. But when that environment changes, those habits become deleterious, they become maladaptive, they no longer fit.
And then, we have to sit down and reason again. What Simon is arguing is now is the time for us to rethink thought, meaning rethink habit. There's a nice language for this. Daniel Kahneman wrote this book on system one and system two, where system one, if you like, is the instinctual, the reflexive, and system two is the analytical, the more ponderous. And what's happened, in a sense, is the crisis has forced us to move behaviors that would normally sit in system one into system two. It's almost as if all these years you've been playing chess, and someone came along and said, "Oh, by the way, the rook now can move on diagonals, and the king can move three squares on the horizontal and vertical, and the pawn actually now behaves like a bishop." Things that you would just have almost done automatically now you have to rethink completely, it's a really sort of difficult thing to have to handle and confront.
I think what the pandemic has done is exactly that: things that we took for granted in society, things that are extraordinarily comforting for us, as human beings, human proximity and conversation and group living have been challenged, and we have to rethink it. Hopefully, we will return to that again, but we might not, and I think that's Simon's deep point, which is that it's time to be analytical again and to reconstitute what becomes habit of the future. One of the things that we can all be doing, as we're all incarcerated in this horrible moment, is to think a little bit about that. I mean, what would I change given that the world might throw at me a catastrophe of this scale again? We don't want to be crazy preppers, we don't want to become paranoid, this is not about stockpiling firearms, which seems to me absolutely ludicrous. But it is about being very thoughtful about a world, where perturbations of this magnitude or slightly less might be more common than we had anticipated, the so-called long tail.
Michael: Yeah, as you've mentioned a few times in this, through your lens on the now suddenly obvious importance of individual citizens to a collective experimental process here… It's been interesting, I've been seeing a lot in the SFI-affiliated economics discussion about how this is changing the assumptions that we make about the balance between labor and capital, for instance. Recently, Suresh Naidu on Quartz was talking about the gig economy and how these essential independent contractors, are suddenly the stone upon which so much well-being is balanced right now. They have a negotiating power that they didn't have six months ago. Marketplace, in their reading group that's going on right now for the CORE Econ textbook that Suresh and Rajiv and Sam Bowles and Wendy Carlin and... who else is in on that one?
David: Those are the dominant ones from SFI.
Michael: Yeah. Marketplace was talking recently about how this is shifting the way that we understand healthcare from something that's conventionally understood as a private good to something that's understood as a public good, because this crisis has made it much more evident than it used to be that wellbeing is something that exists in a network, that if I get sick, it's going to affect your health, your economic wellbeing, and so on. What seems to me to be the trend here is a kind of democratization of the SFI way of thinking, really, an understanding of our individuality as something that is emergent and relational. How do you imagine that moving forward?
David: Yeah. I’d like very much that society became more empirical, more analytical, more cooperative, more prosocial. These are things that we all hope for, but I'm not that optimistic. If you look what happened in 9/11, there was this extraordinary collegiality at the level of the globe, great sympathy, the prospect and possibility of global collaboration. But very quickly, it turned into rabid xenophobia and protectionism, and it sort of all went horribly wrong. I don't think we can place the blame anywhere in particular, but human beings very quickly reverted to their usual selfish selves.
That's why I think this point about analysis versus habit is so crucial because what we sort of have to do is we have to make what is clear when we reason clear when we act, we have to turn thought into action by making analysis habit. Unless we do that, they'll just be a massive rebound to normalcy. I mean, look, we're all looking at oil prices, and it's extraordinary. We’re also looking at the incredible rapid cleaning up of the environment, given the reduced traffic and human activity. But we all know what's going to happen: as soon as a green light goes on, humans will go out and say, "Look how cheap gas is, I'm going to take road trips," and it'll massively increase production. And all of those transient positives that only a little bit offset the terrible negatives of what's happening to people in their lives, and unemployment and in terms of health, will be eradicated.
So I'm not the person who believes in this. I believe that, unless you move from something into a social norm and a habit, it's basically behaving like a Pollyanna to think that the decency that one observes under periods of crisis are maintained in periods of normalcy. So we actually have to act in very thoughtful ways, in order to ensure that that's true, instead of hoping that it will be true.
Michael: Certainly, although were we to rush back into this, we would be running back into the same wall that nailed us the first time. The normal that we would be rushing back into is a mirage, and the second lesson would be even more difficult than the first. So I imagine, over time, run multiple iterations of our foolish and uninhibited return to the great American road trip and business as usual, and it seems like it would select out those behaviors over two or three runs.
David: But you see, I'm much more sympathetic to the desire for normal. The example that I like to give is exercise. One of the things we've learned, I think, in the last several decades, is that everyone wants to be healthier, but it's extremely difficult to change your habits. And there was this perception, say, 20 years ago that you'd have to sort of cut your caloric intake in half, and you'd have to start running 20 miles a day, and so forth. And no one could do it. Now we live in a much more humane world, you can say, "Look, if you take a 10 minute-walk, that's fantastic. That's genuine progress. You're developing habits that you can then build upon." And I think the worst thing that could happen is an alternative ideology, that doesn't like this one, proposes these draconian transformations of society that will be rejected wholesale.
The right thing to do is learn from fitness coaches and say, "Look, it's not a big change, maybe we should stop shaking hands, right? It's not a big change, we should be a little bit more considerate of individuals in the gig economy." So incremental change is real change, revolutionary change is illusion. I think one of the things we learn from complexity is: any attempt to treat the system as if it was simple and that there are one or two levers that will lead to transformation is a mistake, and we have to have a nuanced attitude towards tiny changes in many different places.
Michael: Well, that seems like a great place to tie a bow on it, David. What do we have looking forward into the next week? Do we know whose essays will be coming out next week?
David: Yeah. I mean, it's quite exciting. I think we have some really interesting pieces. I’ll mention a few of them. One is the nature of zoonosis and pathogenesis, where did this virus come from, from the bat population? Why aren't the bats sick, but we are? How does that work? So that will be one. We'll have some insights from the socio-political crisis in Chile. What are the similarities between social upheaval in society and the kinds of experiences we're now having in the pandemic? And then, some very interesting analysis of the use of analogy, in trying to allow us to think through the crisis.
David: So, we've heard mention of this being war and there being a wartime president, we've heard mentioned this is like the plague. What are these analogies doing? Are they useful or are they alarmist? So a whole set of issues in this general complexity perspective, which is looking at everything above and beyond the primary viral agent that's leading to this catastrophe?
Michael: Well, excellent. David, I look forward to discussing the next round of Transmission articles with you here soon.
David: Me too and best of luck to everyone at home. I hope you're being good to each other, and I hope that these ideas helped you in some small way.