It takes effort to embrace complexity. Simple models, simple narratives seem easier up front, their consequences only obvious in retrospect. When we talk about COVID-19 transmission rates, we’re using averages that do not offer crucial insights into how those rates may vary. When we target complex ailments with silver-bullet pharmaceuticals, we don’t address the underlying systems-level problems. Radical uncertainty resists attempts at easy answers, forcing changes in the pace at which we take shots in the dark. Sometimes, as with infection testing, we can’t seem to take shots fast enough.
But understanding systems helps identify good points of intervention, to find the keystone species for a conservation strategy or draw from history the most instructive lessons for today. Understanding human motivation can help us gamify the exercise we need to stave off frailty, and secondary illnesses. A small up-front investment in understanding our society as multi-scale and networked can prevent enormous economic costs.
Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and each week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.
In Transmission, SFI’s new essay series on COVID-19, our community of scientists shares a myriad of complex systems insights on this unprecedented situation. This special supplementary mini-series with SFI President David Krakauer finds 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.
If you value our research and communication efforts, please consider making a one-time or recurring monthly donation at santafe.edu/podcastgive … and/or consider rating and reviewing us at Apple Podcasts. Thank you for listening!
Further Reading:
John Krakauer and Michelle Carlson on COVID and Spiraling Frailty Syndrome
Stefani Crabtree on What History Can Teach Us About Resilience
Van Savage on The Informational Pitfalls of Selective Testing
Cristopher Moore on The Heavy Tail of Outbreaks
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Michael Garfield: All right, David. We're back for week five and this is again, I think if we carry this from the micro scale to the macro scale, there's a lot in here that links to topics that we've discussed on previous podcasts work that's been done by SFI faculty that has not shown up in the show yet. There's a lot to go on here. Let's start with Cris Moore's piece because this one I think connects really cleanly to a lot of work that's being done on, you know, network dynamics and social contagions and so on.
David Krakauer: Yes. So, Cris's brings us right back to the start, to this whole question of R-subzero, R-naught again, which is this number that we don't seem to be able to escape from, which tells us how many secondary infections we expect a primary infection to induce. Cris makes this really important point. If you go online, for example, now, and look up R-naught, you'll see an R-naught number associated with every virus, right? Different infections have different R-naughts. And the first point to make of course is that R-naught is not just a property of a virus; it's a property of its host. So it really doesn't make that much sense to assign an R-naught just to a pathogen, but you can because it's sort of averaged over the human population. And the whole point of Cris's contribution is to point out the dangers of averages.
Most of us know this already. I mean, you don't go into a clothes store and have them stock just the median size, right? The most common size. That would be useless, because they need sizes that are larger and smaller. So you consider the variance as well as the mean. R-zero is a mean in that same sense and he gives the following insight. Imagine that there are parts of the country where R-naught was greater than one and other parts of the country where R-naught was less than one. Or imagine there are populations where it was greater than one and less than one. You would still, when you present the average, conclude that the infection was sub-critical. Now, that means, by the way, a supercritical infection refers to an infection that leads to exponential growth, whereas subcritical is one that will peter out eventually. We've seen that ourselves in our own state here, Michael, because there are populations who might have preexisting conditions or there are all sorts of socioeconomic disparities that might increase the local R-naught. so McKinley County has the highest rate in New Mexico, where there are many Native Americans and so it's very dangerous to be reporting the average. In his contribution he goes through a number of very simple models where he shows that, in the first instance, imagine you have an R-naught that's less than one.
I think in his particular case it's 0.8. Even with that, you expect to see a variance, a distribution of outbreak sizes. If you were to observe the outbreak independently and say a different localities, most of them would be small. About 1% of them would be about 50 and in one of those localities it may go up as high as 80 even though the expected numbers with an R-naught of 0.8 would be five on average. So the average is dangerous because of regional variation. And then there's regional variation, if you like, in individuality, the so-called super-spreader and the super-spreader is an individual who infects many more people than the average. If you have a super-spreader that can generate, say, 20 secondary infections — in those cases you can have, even though the average is still low, the average outbreak might still be say five with you could have hundreds of individuals being infected. So the story here for Cris is: beware of the average, consider the variance, which leads you to a contemplation of what's called the heavy tail that there are in the, if you like, probability distribution outliers that are more common than you might have anticipated.
Michael Garfield: This links to the conversation I had with David Kinney on bringing in economic or energetic considerations into the issue of granularity in scientific investigation. At what level of resolution should we be asking the question? Or, to see it sort of inside out from that way, Mirta's work on the danger of drawing global conclusions from local samples and how by only looking at our immediate networks, we generate extraordinary biases about the world at large. Something in Cris's piece that I really appreciate is how it draws our attention back to something I see discussed a lot at SFI about multi-scale considerations. And you know, we've addressed this on the show a few times already. For example, the work that you and your co-authors just did on the information theory of individuality and the importance of being able to think of the individual as something that, you know, might be more conventionally understood, like a single person or perhaps an entire society. Like, how can we rigorously ask the question of whether this thing is bound as an individual in that way? So, you know, when he talks about the difference between thinking of this super-spreader individual versus super-spreading events and situations, I think that starts to give a sense of the importance of the multi-scale view in complex thinking as well as how that provides an entry point for asking at what scale should we be considering this in order to make the most useful pronouncements.
David Krakauer: Yeah, it is very important to bear that in mind, because you'll often observe that there are discrepancies between what you observe in your own community versus what's being reported on the news, which tends to be an average. And I think we have to develop a sophistication that allows us to understand how those two things relate. So, the national average might not be what you're experiencing at all. You might have a much harder time. And Cris's contribution gives us a quantitative insight into why that is expected to be true.
Michael Garfield: Actually, I think this links pretty cleanly to something I want to discuss with you later in Stefani Crabtree's piece. But let's put a pin in that and let's move to the piece by Van Savage on testing capacity and the dangers of not being able to meet the actual spread of the disease with the number of tests we're administering.
David Krakauer: Yeah. So, you know, we discussed David's contribution last week about the whole challenge of inferring what the true, false positive–false negative rates on tests are. But there's another issue in relation to testing that Van raises, relating to the other great phrase that we're all hearing regularly, which is "flattening the curve." Now of course, "flattening the curve" refers to the time-dependent daily, weekly, monthly incidence or hospitalization rate and what contributes to that. And of course we will understand that we're isolating so as to minimize the transmissibility of the pathogen and thereby its negative consequences, hence "flattening the curve." But how do we know? The way we know is by testing and, Bayesian challenges aside, the point that Van makes is that there's an even more profound rudimentary constraint, which is who you test and the number of tests that you possess.
And he just makes this obvious point, right, that if the true number of cases is beyond the maximum testing capacity — which it is — and, in addition, you're testing people who present with symptoms — which we are — then what you're ultimately doing is reporting on noise. And the curve will appear to be flattening for pathological reasons. Let me just explain that a little bit more clearly. If the number of tests is much lower than the true number of infected, then what you're really reporting on is the number of tests, not the true underlying incidence. Take a sporting example. For example, the Rose Bowl in Pasadena or Wembley Stadium in London. Both of those hold about 90,000 fans. Let's say that you filled the stadium. You wouldn't conclude that there were only 90,000 football fans in the U.S. or U.K. — of course, they're different kinds of football fans — that you'd somehow reached the maximum popularity of their respective sports because they're saturating. And so the signal there is not truly informative of the true demand. It's limited supply. And the only way you could truly estimate how popular those two forms of football were would be to take random samples across stadia from all over the world. And that's precisely Van's point.
Michael Garfield: Again, you know, this is compounded by the issue that so many of the COVID-19 cases are asymptomatic, you know, so it really does make filtering, trying to focus who and how we test a much more complicated issue, and it really does bring us back around to the importance of random testing.
David Krakauer: Exactly. The other point that Van makes is this issue of the exponentials. You know, a disease could be increasing exponentially and you have enough test kits to observe that or it could be that you have a huge number of infecteds that we hadn't realized — as you said, who were asymptomatic — but the testing capability is increasing exponentially, in which case it would look like exponential growth. Whereas in fact you'd already have saturated the numbers who are infected. So there are these interesting, if you like, perplexities of testing that you have to bear in mind. He goes on to present some simulations where he shows just how bad this problem is. You know, folks can read through those studies. But the key point here is there are only two options available to us: We either massively increase the testing capability or we test randomly.
This brings us, I think, to a general point I want to make again, which we've been doing throughout this series about the philosophy of the models and theory. When Van presents his simple simulations to demonstrate the challenge that we face, what he's doing is he's generating what we would call a null model. That is, we don't really know how to interpret data unless we have an expectation, and the expectation is established by a rigorous simulation. And we've talked about throughout this series other kinds of considerations. We've talked about the prior criterion. We've talked about issues in Cris's contribution about what I call the structure criterion, that is, the assumption of the mean field or the average. And so we're getting a bit of an instruction I think over the course of this pandemic into how model building actually works and what we should consider reliable and what we should remain suspicious of.
Michael Garfield: Yeah, definitely. You know, just to generalize this a bit, for people who are more familiar with similar problems and related domains, this kind of problem comes up a lot when we're asking about changes in the incidence of diseases like cancer over the last 150 years, you know, or anything involving a spotty archaeological or paleontological, geological record that changes in species' distributions or volcanic eruptions over time. It brings us back to the same question that vans asking about whether we actually are observing a difference in the frequency of these events or whether we're observing an improvement in our ability to actually measure them. As that becomes harder and harder to figure, it brings us into the next piece, on making good decisions under uncertainty. This is a really cool piece because this is four authors, all of whom are focused on this particular issue of radical uncertainty. Why don't you lead us into this?
David Krakauer: Yeah, so this is a very active area of research, often associated with one of the authors here, Lenny Smith, and of course there are four of them, on this question of what do you do when you really don't know. That's what radical uncertainty is meant to convey, where the quantification of a cost and a consequence is highly contestable. But, nevertheless, you have to reach a conclusion and implement a policy. And what they present in this contribution is the point that very frequently the best statistical model that you could generate in earnestness with the best data that you have to hand will be vastly less informative in a deep sense than the cone of all possibilities. You know, in other words, it's a bit like Cris's point — the difference in the mean and the variance. You be better off knowing the variance than the mean. And we've seen this, of course, you know, yesterday U.S. corona-correlated deaths were about 60,000, and that was a number that surprised a lot of people because it wasn't expected to reach that level until about August.
And of course that's right because we had radical uncertainty about the projected trajectory. So, the question then is, if you can't report on probabilities because their data just aren't good enough to support that, what can you do? And this community has been arguing that, in addition to training people to get used to these presentations of the cone of uncertainty, the variances, and the higher moments of the distribution, but also to think about credible, what they call conviction narratives, which is you have to reach a conclusion. The data is not good enough, but a decision needs to be forthcoming. What are you going to do? And most of us, of course, in positions of leadership or having to make decisions, do this all the time. We weigh to the best of our ability these factors, and then we contrive or invent a narrative that makes sense of them that doesn't appear irrational or totalitarian.
This style of reasoning has become very popular. Actually, this year a book was written by John Kay and Mervyn King — and John, of course, is a frequent visitor to the Santa Fe Institute, very prominent economist. They wrote a book called Radical Uncertainty. In that book they do a very good job in some sense of dethroning the reigning probabilistic connotative school when it comes to domains of uncertainty. But they don't do such a good job in replacing it. They also argue for conviction narratives. And I'm very conscious of the fact that narratives can be very, very dangerous because they're very subject to cognitive biases. And we've talked about these in the past — you know, attribution biases, confirmation biases, framing biases, and so forth. And so I think my question in reading this was, it's a very compelling piece is what else is there?
I think we can take some pages out of mathematics. There are ways of reaching certainty, which have nothing to do with quantification. And people forget that. People often confound mathematical reasoning with quantitative reasoning. It's not. The Greek proofs in mathematics are very rarely quantitative; think about proofs in typology or geometry or logic. So I think what this raises for me is really a conversation about how we reach reasonable conclusions where quantity doesn't dominate, where we recognize the limitations of narrative. Are there other formalisms we might explore which give us means of acting without assigning numbers? And I think there are, actually, and this is I guess a conversation I would like to have with the authors.
Michael Garfield: This evoked for me — and I'm curious how you see this — in business culture, there's the famous OODA loop, you know, observe, orient, decide, act that was developed by a United States Air Force colonel, John Boyd. You know, this is how to make decisions rapidly under conditions of great uncertainty as a fighter pilot. And that's where he said, you know, if you think about your narrative as the direction you're pointing the plane, right? You know, calling back to Manfred Laublicher's piece on fitness landscapes, the authors for this piece say, crucially, optimization in radical uncertainty is dangerous because its premises are not satisfied on a fitness landscape. It's like, in a period of cataclysmic change, the fitness landscape shifts, as we have discussed in previous episodes. There are valleys where mountains used to be. And if you continue to optimize for the mountain of your narrative, then you're going to walk off a cliff.
David Krakauer: Yeah. You know it's very difficult, because we don't really have many alternatives. I was very convinced in about, I think it was 2018, I read a book called The Tyranny of Metrics by Jerry Muller. And he's basically making this point that we've gone from measuring performance to fixating on measurement itself. Science is full of this. I mean, most of us are nauseated by this idea of the H-index, which is a means of measuring scientific popularity, and that's the key point. It measures scientific popularity, not scientific profundity, and these indices which have been proliferating across different sectors are essentially a substitute for judgment. I do think that this article raises a question that we have to address in all walks of life, where quantification is spurious rigor and I feel it's that important that we should at the Institute be thinking very carefully about it. I tend to like logical simulation environments generating, if you like, the cone of uncertainty through a principled set of null models which at least gives you a sense of the full space of counterfactuals which you can then use to navigate. But I think it's very open territory.
Michael Garfield: As you did last week, calling to orthogonal discussions that we've held at InterPlanetary festival. Your piece on counterfactuals and null models is a callback to the world-building panel that we held last year with James S. A. Corey and Rebecca Roanhorse and Michael Drought on how the work of science-fiction authors helps us do this in a non-quantitative way. Allows us to speculate — and this is possibly why the work of science-fiction authors has become so useful in a very rapidly moving business environment in helping us map the full spectrum of possible scenarios. Right? Like that's why you've got Neal Stephenson on the list of former Miller Scholars.
David Krakauer: Yeah, that's exactly right. I mean, I've described narrative as ontology engines a number of times. I mean, I think that is actually what they mean, the authors, by narrative and conviction narratives. I think great authors do precisely that. And Neal is a great example of someone who has some of the most sophisticated reasoning about the near-term future of the economy, say, or technologies, because he's exploring that counterfactual space. But I think that there must be alternatives to that, that we could somehow conjoin the best of narrative with the best of modeling to create a real engine that allows us to generate alternative possibilities. And I don't think we have such a thing yet.
Michael Garfield: Let's act as if we can. You know, moving from that, let's take a dog leg here. It's a little harder to find a clear through line into the work by your brother John Krakauer and Michelle Carlson. But it's a really interesting piece on spiraling frailty and the importance of physical exercise encountering some of the worst effects of this disease.
David Krakauer: Yeah, so, obviously, I'm somewhat biased. It's a very interesting article, partly because it was written by my brother and a colleague, but what they're raising is the question I think that many of us are asking, sitting at home: How do we remain active? How do we remain healthy? And these alarming observations that the COVID virus, in addition to immobilizing very many of us, is actually attacking individuals who have conditions that are compounded through sedentary lifestyles. So the article begins with this discussion of frailty, which is a real biological syndrome that about 10% of Americans have, that's associated with various clinical components, including, you know, muscle tissue loss, a slowing down of gait, a significant reduction of physical activity, that feeling of always being low energy or being exhausted and so forth. And this syndrome increases markedly over the age of 65 and we can't but notice that the age of 65 is that kind of magic category where you become significantly more susceptible to the coronavirus.
It's the group of 65 up that are most vulnerable to this infection and are experiencing the highest hospitalization rates. So it looks as if there is a synergy between these two things and the frailty is often unmasked or amplified by a perturbation such as this infection. In the medical community, John and Michelle make this point that there is a preference to treat conditions with monotherapies. We can attack that single system with a single drug and the fact is that most of us know it doesn't work. Right? And in the case of energy, estrogen or testosterone replacement therapy would be a good example. And they cite the work of Linda Fried, who defined frailty syndrome, and she makes this really interesting point that the extraordinary thing about physical activity is that it's not a monotherapy. It actually up-regulates many systems that are necessary to maintain health and they're particularly effective at ameliorating two chronic diseases, hypertension and type-two diabetes that are the primary co-morbidities with coronavirus infection.
So it's really quite deep. It's not just you're better off exercising because it will reduce your susceptibility, but those groups, if you like, that are most susceptible stand to benefit most. I think that point was really important. The other point that they make is how might we intervene on the system that is the entire-body system. And one point that they're making here is that there is a reluctance to engage in physical activity. It can be psychologically trying. You might feel as if you're not healthy enough even to begin, and John has been a very strong advocate for game-like environments which you can conduct without judgment, which are extraordinarily fun and can encourage relatively high levels of mobility inside the house. Think about things like Wii Sports or Beat Saber or Ring Fit Adventure. All of these are games which ask you to move. They're fun to engage in, the activation energy required to engage in them is relatively low, like going for a five- or six-mile run. And I think they're arguing that perhaps this kind of technology might be important complement to regular forms of physical activity outside in times when you're being asked to socially isolate.
Michael Garfield: It's funny in kind of a similar way to the way that this pandemic has driven so many social gatherings online kind of prematurely before we can really engage in a robust simulacrum of what it actually to meet with one another in embodied physical spaces. It feels like we're right at the point here where this has happened to us early enough that we're still sort of climbing up from base camp in terms of the way that embodied physical computing — you know, the actual potential here. The plans that were popularized by films like Minority Report where you're not just sitting at a desk typing on a thing as as I and several of us at SFI are now trapped in a sedentary work environment and really have to force ourselves out of the seat. But, in addition to the games that you listed, you know, I've been seeing ads for games like these balancing boogie board-type deals with video games built in, where the video game controller is your body. And that's really exciting. But you know, to your first point, I feel like what they're getting at is something that I see brought up again and again in the underlying hidden patterns, the generalities that are opened up and revealed through complex-systems science, specifically in relationship to understanding things, again, as multi-scale networks. And this piece in particular reminded me of the work of Jennifer Dunne on trophic networks and the conversations that we had about this way back in episodes five and six. You know, where it's like, if you think about the causal relationships in the body, like a food web, then physical exercise might be like the keystone species. If you remove it then the entire web collapses, and it's really important to identify it if you know in the, analogically, the conservation efforts that you apply to your body ecology.
David Krakauer: Yeah, I think it's this very interesting shift of perspective that comes with familiarity with complexity. This notion of a systemic intervention. You know, in their article they cite this new book by Peter Sterling called What is Health? And he makes this point that a complex therapeutic intervention — and he's interested in the case of hypertension or diabetes — is not that you begin with a antihypertensive or anti-diabetic polypharmacological intervention, but you do exercise, right? You move. And it seems to many people a little counterintuitive. And there is something that needs to be explained about our reluctance to engage in activities that are extraordinarily beneficial to health, which have very low costs. These are not pharmacological interventions. Why are we reluctant to take those, but we are so willing to spend huge sums of money on pills which do far, far less? And I think one of the areas where this has been very obvious has been depression, right?
So this is a real area of concern. We treat them with antidepressants, they have significant side effects, but one of the most effective means of treating depression is with exercise. And so I think that John and Michelle are getting at a really interesting point: are there technologies like gaming, for example, which would make exercise feel as legit as taking a drug? Somehow hanging them on an armature of engineering gives them a placebo-like salience that they might not have if we just step outside to go for a run. And this is a psychological challenge for us that we all need to face.
Michael Garfield: Yeah. I wonder to what extent this is due to the fact that we have sort of eroded the topsoil culturally in terms of the ways that we're able to look to the lessons of human history for how people in other cultures have addressed comparable issues in their own times. You know, that we tend to think of our time as radically different than other moments in history. And so we leave questions about the innate benefits of health and well-being to someone through gardening and the way that gardening helps cultivate a person's microbiome and exposes them to sunlight and keeps them physically active and so on. Because we're not sort of economically motivated to draw those analogies or we're motivated to believe that our times are unique and call for modern, high-technological interventions.
David Krakauer: Yeah. You know, it's interesting, Michael. I think it comes down to insufficient appreciation of complexity. I like to think about this in terms of causality and I think you raised it, which is, we're energy-minimizing cognitive systems. And if we can find dominant mono-causal explanations, we prefer them and probably for good reasons. But that leads us astray and it does lend a cognitive bias to us which disposes us towards solutions that look as if they're single silver bullets. And what we now know, I think, about health, about mental health, physical health, is that they are the convergence of many different systems and that they have to be treated with something as complex as they are. Right? It's a little bit like education that we discussed in relation to Carrie's. You can pretend that learning is simple and have a correspondingly simple pedagogy or you can recognize it's not and explore the full panoply of possibilities. And I think it's exactly the same here. Medicine will evolve by recognizing the complexity of the body and pursuing complex interventions. Physical exercise and sleep are two very good examples of that.
Michael Garfield: So yeah, again, there's a link here to the first piece we discussed in this call and Cris's caution around R-naught, the average, is exactly what you're saying here. It appeals to our desire for a simple story and the link to David Kinney's work and his insistence on the inclusion of context in our philosophy of how we address specific scientific questions, that it's unpopular in philosophy to insist that there's a "yeah, but," that there's conditional considerations. People want to be able to make sweeping universal claims. And the reality is that that's, you know, insufficiently detailed, right? I mean, it's not going to get us there.
David Krakauer: Yeah, I agree. I agree with that.
Michael Garfield: So let's move on to Stefani Crabtree's piece. I think this is a really simple and inspiring statement that she is making about the importance of reflecting on historical precedent here.
David Krakauer: Yeah, I mean, we had already talked in relation to the frightening economic downturn that this is one depression among many or one opportunity among many and the same goes for the experience of plagues and epidemics and famines and Stefani opens her piece by making the observation that the practice of quarantine that we are now experiencing derives from a 14th-century Venetian convention of keeping ships at harbor to protect the citizens of the city-state from pathogens. And she goes on to discuss the plague of Athens in the fifth century, which killed one-third of the city-state's population. And the value of narrative, of historical narrative, by Thucydides and others in helping us come to terms with that plague. How in fact he describes the spread of fear as moving more quickly than the spread of the plague. It's something that we discussed in relation to the infection of memes as opposed to the infection of viral genes.
And so there is a historical context that all civilizations have experienced and learned to cope with. But I think where the article becomes more serious, which is something that we haven't yet discussed in as much detail as I'm sure we will, is in terms of the social upheavals and implications of these plagues. Stefani herself has worked for a long time on Southwestern archaeology, in particular studying the Chaco culture. And this is the culture right here, very close to us, which thrived between the 10th and the 12th centuries, but experienced a very precipitous decline when the ancestral Puebloans left. And there are all sorts of hypotheses for why this is true, many of them having to do with drought and severe shortages in protein. But the point is it led to significant escalations in warfare and violence and that connection, not just between disease and markets, but between disease and civil unrest, is something that I'm sure is at the back of many of our minds.
So that's a very important point. I was reminded, in fact, when reading through Stefani's, of a book that I read a couple of years ago by Kyle Harper called The Fate of Rome, and the great book on The Decline and Fall of the Roman Empire was written by Edward Gibbon. And in Gibbon's book, the empire declines for two reasons, really. One is self-destructive religious superstition, and the other is warfare at its boundaries. And what Harper is arguing is that we've overstated those social factors and underestimated the importance of of climate and epidemics. And of course he talks at length about the Antonine Plague, which was in the second century A.D., where a thriving Roman Empire under Augustus was brought to its knees by smallpox. It's a little bit like our bull market that was brought to its knees by the coronavirus. And so that's another example where we tend to underestimate certain invisible factors, certain complex causes, in favor of the standard explanations and what Kyle Harper goes on to do, actually, in that book is show that, right after the Antonine plague, just as Roman society was recovering, Rome experienced what's called the late antique little ice age, which was very, very unfavorable to agriculture. And so one catastrophe came on the heels of another and that compounding effect really brought the empire down, as opposed to the standard Gibbon explanation. Many of us are talking about climate now and if the climate catastrophe should begin to become more apparent on the heels of the coronavirus catastrophe, society is in for a real battle.
Michael Garfield: Yeah. It'll look somewhat similar to coronavirus infecting someone who's already suffering from spiraling frailty. Right. Just for listeners, you know, Kyle Harper gave a presentation at our symposium last year on this book that was extremely fascinating and I think can be filed under a long list of rather prophetic presentations by people at SFI including Lauren Ancel Meyers's work on preventing the next pandemic for last year's Ulam lectures. And you can find all of that stuff on our YouTube channel if you want to go deeper into that.
David Krakauer: Yeah, Michael, I do want to point out, I think that you make a really good point about non-obvious analogies between Kyle Harper's work and John and Michelle's points, which again relate to this complex-systems perspective, that it's very rarely the case that interesting or dramatic phenomena have simple causes. And one of the dangers, I think, of the current crisis is this collapse of the dimension of causality to a single cause, that is, the virus. Forgetting that much of what we're observing is pre-existing inequalities of susceptibility or economic circumstance amplifying the effects of a virus. And that is important to bear in mind. And I'm hoping that this series is contributing to that kind of insight.
Michael Garfield: You know, relatedly, to call back to Doug Erwin's commentary on mass extinctions. You know, I think most people similarly look for a silver bullet when we're trying to understand, you know, earth history. And over the last 15 years or so there's been a shift in the way that we understand mass extinctions as complex events that are caused by the compounding of factors like we're talking about here. Most people, thanks to Louis and Walter Alvarez, have it in their minds that the end Cretaceous mass extinction was triggered by an asteroid impact. But at the same time, this was concurrent with a 2-million-year volcanic eruption in what is now India that caused climate change, that there were changes in atmospheric and ocean chemistry, there were the emergence of land bridges. You know, when I was studying this stuff, my mentor Robert Bakker talked a lot about the way that organisms were changing their migration patterns and he really insisted that pandemic disease transmission also probably played a very large role in the extinction of the dinosaurs. You know.
So, again, it's just yet another example where like you said, we have to be careful about oversimplifying the story here.
David Krakauer: Yeah, yeah, absolutely.
Michael Garfield: There's one more link I'd like to make because you brought it up with Stefani's piece. When she's talking about the fall of the Chaco Canyon society, she mentions that, when productivity failed, the Chacoan hierarchical society fractured, violence increased, and society reorganized into regional communities that issued hierarchy. So this calls back to Miguel Fuentes and his work on how social graphs disintegrate in crisis, and Tony Eagan's piece on the balance between federal and state governance and disasters, and how you suggested that maybe acclimating to the challenges that we're facing today might require dynamic constitutions that can become more or less strict in their regulation depending on the needs of the moment. I feel like we're at a point in the series now where we can start to trace the outline of a more general systems way of thinking about these things.
You know, that we can understand why it would be that the societies that came up after the fall of Rome or after the fall of Chaco or the sort of specter of Balkanization that's lingering over us now are actually not just a failure of a coherent system, but actually an adaptation to a crisis that's happening faster than the latencies of a massive and unified institutional response can address with respect to on-the-ground conditions. It becomes very difficult for the general to conduct the war when something bizarre is happening rapidly on every front, more emergency local elections, more self-organizing duocracies that we see spring up in disaster relief. I'm curious how you understand this in terms of the insights that complexity science can offer us in adaptive strategies for social organization on the other side of this.
David Krakauer: Yeah. I think one useful mantra might be, complex causality requires complex control or complex intervention and the history of thought is a history of low-hanging fruit. We start with the simplest things that we can intervene into in the simplest way. We see an apple fall from a tree and with remarkable ingenuity arrive at a theory of gravity. We develop engineering techniques of ballistics that are mirrors of that world. But when you get to the complex world, things don't work that way anymore. Things are connected in subtle ways. There are non-linearities. There are different phases and critical transitions and we're just at the baby steps of understanding how to intervene into complex systems. And the last thing we want to do is pretend that they're simple systems. And I think that's what we have been doing. And I think a number of these contributions that we've been reading over the last few weeks are pointing out the challenges and dangers of doing that. And today we've arrived at this point where we're getting an insight into what systemic intervention and control might look like. And the good news is it's available to us and available to us not at considerable cost, at least in relation to health.
Michael Garfield: So where are we going with this next week? What do we have on the table?
David Krakauer: Well, that will be an exciting one. Next week we'll be discussing cities, cities as the engines, if you like, of the pandemic, but also potentially as the creative centers that might arrive at solutions. We'll be talking about mechanism design, how an economy functions when it doesn't have a market and what we can replace the market with. The return of the wild. That world out there that we have left to itself has been doing remarkably well without us. The uncertainties associated with exponential processes and the dark matter and dark energy of microbial life.
Michael Garfield: Well, I look forward to that and I'll talk to you next week.
David Krakauer: Thank you.