It may be a cliché, but it’s a timeless truth regardless: who you know matters. The connectedness of actors in a network tells us not just who wields the power in societies and markets, but also how new information spreads through a community and how resilient economic systems are to major shocks. One of the pillars of a complex systems understanding is the network science that reveals how structural differences lead to (or help counter) inequality and why a good idea alone can’t change the world. As human beings, who we are is shaped by those around us — not just our relationships to them but their relationships to one another. And the topology of human networks governs everything from the diffusion of fake news to cascading bank failures to the popularity of social influencers and their habits to the potency of economic interventions. To learn about your place amidst the networks of your life is to awaken to the hidden seams of human culture and the flows of energy that organize our world.
This week’s guest is SFI External Professor Matthew O. Jackson, William D. Eberle Professor of Economics at Stanford University and senior fellow of CIFAR, also a Member of the National Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences. In this episode, we discuss key insights from his book, The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors.
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Matthew Jackson’s Stanford Homepage.
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Michael: Well, Matthew Jackson, it's a pleasure to have you on Complexity.
Matthew: Thanks, Mike. It's great to be here.
Michael: So I just finished reading this spectacular book of yours, the human network, how your social position determines your power, beliefs and behaviors. And I was pleased to discover that in spite of the branding, that this is not some sort of light playful self-help thing.
This is really a potent introduction to network science. And so I would just love to go through this book today with you and, have you exposit on some of the core ideas?
Matthew: Sure, yes. Wonderful.
Michael: But before we get into that, I'd like to know how you got into the work that you're doing in the first place. How did you become an economist and what drew you into this kind of research?
Matthew: It's a winding path, I guess. And a little bit serendipitous, I started out more or less interested in mathematics and then. Branched into economics because it was a place where I could apply a lot of the tools that I was learning in math and eventually got interested in networks more or less by accident.
It's having a conversation with a friend about power and trying to understand power, and then starting to read the sociology literature on relationships, and that led us into trying to understand how people acquire powerful positions and what that actually means. And that, that was sort of the starting point.
And then that sort of sent me on an Odyssey of trying to understand how social structure impacts behavior.
Michael: Wonderful. Yeah. Towards the end of your first chapter here is on power and influence. And so that seems like the right place to start. You talk about four different kinds of centrality, and I'd love to hear you break those down a little bit for us.
Matthew: Sure. I mean, I guess the, the most basic we all think of is just popularity. You know, how many people you connect with and that's very natural. You know, we, we count how many friends we have on Facebook or how many followers you have on Twitter, and that, that gives some idea of a reach of a person and so forth.
And that can be very powerful and in marketing certain kinds of things where you just want to get something out quickly and at a fairly shallow level...then once you push a bit deeper than you start thinking about other kinds of ways in which people can be well connected.
And so another way that is very important is not just having many friends but having well-connected friends. And that leads to a notion that's sort of somewhat circular in a sense that your influence comes from the influence of your friends and their influence comes from the influence of their friends and so forth.
And so you end up with a system, but that begins to bring the network into the picture now, right? Because now I care about not only my direct connections, but my indirect connections and that that kind of influence is something that, you know, I guess probably the best example of, of where that really came up was in Google's Page Rank.
So that system is built off of a similar mathematics to this kind of idea of indirect relationships and influence. And that...I don't know how many people remember when you first started out using search engines, but they were often pretty lousy at the time. And when Google came along, it was just eye opening in terms of how much better it was.
And what it actually did was it looked for pages in the internet that were central in this way, not just having lots of connections, but are having well connected connections. And that was sort of a key insight that it had. And you know, you were finding the pages that the most influential pages wanted to find, not just that lots of pages wanted to find, and that really made a huge difference.
So that's sort of a second type. And then a third type is one that's often known as is betweenness centrality, which looks at how well a person is situated as a connector of other groups that might not otherwise have strong connections to each other. And so there's a question of, am I a strong intermediary? Am I somebody who is...sociologists call it brokerage. Am I a broker? Am I a person who is bringing information or important opportunities and access from one group to another so that's another type of important influence that one can have in terms of their position.
And then another is just, you know, it's sort of how, how good am I as a spreader of things. And I've worked on that quite a bit. It's sort of an idea of, "How good am I as a diffuser of information? Can I diffuse information? Well, it is going to spread through the network well? "And that means that I have to have. Some good indirect connections, but it's not sort of an infinite process. It's how well am I connected in terms of my friends of friends and friends of friends of friends, and am I well positioned to get information out and and have people understand it and believe it.
Michael: I really like how in this book you explain that reach is sort of this real world midway zone between popularity and the strength of your, or the power of your, connections. That we only have so much pull, you know, as another way of thinking about that, that an idea decays as it spreads through the network and it's you know, like old news or old bread, that kind of thing.
And in that sense, it's curious to imagine what a search engine that adds that time dimension would look like, rather than, you know, the sort of God's eye view that Google has, but like how we're actually witnessing links flowing through these networks.
Matthew: Right, right, right. And actually, I think, you know, a lot of times when you think of things, you know, people think of things going viral or spreading, most of the time things die out.
But there's a lot of times where they die out in that middle zone, right? It's not that it's getting billions of views, but it's also not getting 10 Things will make it a few hops away and that's a very common situation. And then if you really want to understand, if somebody is getting something out to a reasonably large community that can often just be two or three steps away from them.
And then after that, you know, things can, can really start. You know, time takes over and people, whatever the topic is, it might become stale by the time it starts spreading further. And there's also a noise that comes into this. So we've looked at this, for instance, in, in diffusion about information about microfinance.
We did a study in rural India, and tried to understand how people were spreading news about a microfinance opportunity that was coming into villages and the best fit by far in terms of how far things spread was really three steps from the initial people that were informed. And after that it seemed that, you know, either it just died out in terms of people no longer finding it.
Interesting to talk about or, you know, it was becoming noisy enough that it wasn't clear what was being communicated anymore. So, you know, word of mouth lasts some distance and then it really starts to decay. And you can look at that decay function and map it out.
Michael: Maybe we're cutting ahead of ourselves, but this is related to some discussion later in this book about the diffusion of disease.
And, I found it interesting to note. That the rate at which different diseases diffuse has everything to do with host-carrier dependencies. This gets into, you know, questions about cumulative culture rather than just the cultural half-life that you just mentioned, the forgetting of the network. That storage outside, you know, media-independent. you know, so i t doesn't require direct transmission and like, something can sit in a hard drive for decades.
Michael: You know, there are interesting ways in which this allows us to research the way that we hold store and act upon knowledge.
Matthew: Right. Yeah, and I think it also points to the, there's different kinds of knowledge and often we think about people learning in simple diffusion processes, but some things, it takes a lot of contact and a lot of information has to change hands. So, you know, if it's just awareness of something that's pretty easy, that's really like a simple flu contagion.
But if it's something where I really have to learn deeply about something before I'm willing to act. So, you know, in microfinance, it turns out that people already knew a lot about microfinance. They just had to become aware that it was coming to their village. But, you know, we've been working with vaccination programs and trying to convince people to get their children vaccinated.
And that's a whole education program. And it depends on who you hear it from and how many times you hear it. And you know, that's a very different dynamic in terms of the, what it takes to convince somebody of something, then, you know, sort of a simple spread. So these things really make a big difference.
Michael: We are definitely cutting ahead of ourselves here but I would love to use this opportunity to talk about. The DeGroot dynamics in these kinds of systems and how news bounces around and you know how we end up misestimating consensus in this kind of thing. It was a very fascinating piece of the book for me. Anyway, I'd love to hear you talk about that.
Matthew: Sure. Yeah. Morris DeGroot is a statistician. He built a very simple model where the way in which you modeled people learning was, you know, repeatedly talking to people. So I talk to my friends and then I hear what they have to say, and. You can think of people as just counters in the sense that I hear different things.
Maybe it's something about a new movie, and I'll hear what each person's opinion is about this new movie, and I'll tend to just average those, right? So if I hear something from five people that this is really great, that I think, wow, that's five pieces of information that, you know, make it sound like this is really great.
This sort of bouncing around that you're talking about could be that, you know, maybe my five friends who all say this is a great movie, all read the same review or all talked to the, you know, the same other person. And we're just natural counters and it's very difficult for us to put a proper weight on these things.
And so, you know, we can be hearing things, these sort of echoes through the network and that can help reinforce an idea and it then takes root in us and hearing things enough times makes a big difference.
Michael: Yeah. Specifically, talking about the ways that information doubles and echoes in these networks reminded me of the conversation I had with Rajiv Sethi about stereotyping and attentional bias.
Cause you know, it's so funny. You talk about how a DeGroot learning model is a kind of scary accurate model for the way that human news travels... We'd like to give ourselves more credit than this, but our working memory, our attention, the algorithms that seem to be at work in human social learning are actually remarkably simple, and from a network point of view, are actually a lot simpler than we experience ourselves to be.
Matthew: Right. Yeah. And it's actually interesting because I think. One thing , I did some work on studying net model with, with Ben Golub. And one thing that we found was, you know, in a world, so if you have a network that's really well balanced, so imagine everybody has 10 friends and they all have 10 friends and everything's really well balanced, it can be that a society with lots of information spread all over the place can still aggregate that well, right? So if I just keep averaging and do something really naïve, right? Just counting. I just keep counting and you know, if I hear something more times than something else, I'll upweight that and down-weight the other thing. I can come to really accurate expectations if I do that. The problem is that our networks don't look like this well-balanced, nicely spread out tree that zooms out into the world. Instead. we are often in fairly tightly knit cliques that are organized by ethnicity and gender and age and profession and religion, you know.
So we're really in these tight knit groups where we tend to get the same information and then it bounces around in these smaller areas where we're intensely talking and keep hearing the same thing, and that that gets reinforced and that's where we go wrong.
Michael: You know, this is actually a great place to back into some of these bigger economic and power questions, right?
Because, you talk about this kind of social learning and how, like you just said, a network in which connections are distributed more evenly can lead to better aggregate estimations of the ground truth of what's actually going on here. But the world that we have is one, you know, where, in part because of the economics of scale, and convenience, you bring up preferential attachment and how it's easier to trust someone that other people we know trust…
And so we ended up in these herding dynamics where I think about... You know, I run social media for SFI and so you see these enormous Twitter profiles, and these are people that arguably are just famous because they're famous. And yet they are shifting public sentiment in remarkable ways.
They're whales of attention and you get in, you know, I think there's an interesting link between questions about economic inequality and questions of attentional inequality, and how do we ensure that people are not getting more attention than is healthy for society?
Matthew: Right. Yeah. I mean, as you mentioned, things that get retweeted are easier to find, so there's this sort of natural building process where we get feedback effects that the more followers you have, the more followers you gain.
And the more attention one gets, the easier it is for, for people to keep following. That kind of snowball effect does lead to this really large social disparity, as you're saying. So it's sort of a social and attentional inequality as much as resource inequality.
This is a valuable resource. And, for a long time, people in network science had been making up a point of the fact that when you look at networks, it's not just evenly distributed networks. And it's not even something where we just sort of rolled dice and some people got a little bit luckier. You see this extra feedback effect with some people having enormous numbers of followers and other people not. And that makes it really hard for a society to process information. These people can have really outsized influences on our beliefs.
Michael: So here's what I think seems at first, like a relatively innocuous example.
You know, you tell great stories to anchor this in the concrete in this book, and one of them in particular is about the wine critic, Robert Parker. And I'd love to hear you explain a little bit why he became such a success, but then also why that became a problem for the wine industry.
Matthew: Yeah. Yeah. I mean, it's, he's a fascinating example where in one quote from one of the wine distributors talks about how his giving a high rating can be worth millions of euros on the wine. And it's just incredible how much influence he ended up having. And, you know, he sort of started out as a, a person who had a different scale that he was using, a hundred point scale, and he was doing things a little differently. But he ended up calling out a couple of vintages that the rest of the critics were saying weren't going to be that good. And he, he spotted them earlier and he sort of made reputation early on in his career and it just took off.
And again, it's a sort of thing where, you know, people are looking for focal points. A lot of times in markets, people are trying to figure out what's, what's something worth and. And in the wine market that's particularly difficult, right? You've got thousands, tens of thousands of different wines, and you're trying to figure out what they're all worth.
And so you need some anchor initially in pricing these things, and he became an anchor and then people started looking to him to figure out what was something that was going to be worth, and if he said it was going to be a valuable vintage then people started thinking, wow, you know, this is going to be great. Parker says it is great. And moreover, even if I didn't believe it, I could still believe that everybody else was going to believe Parker. Right? And you end up with these kinds of self-referential systems that, that Keynes talked about and made famous. And he really ended up having a huge influence on the wine industry.
And as you point out then, then there were the distortions because then wine makers. Tried to make their wines something that Parker would like. Right? So they called it the Parkerization of wines. He’d like these big, bold, high alcohol wines. And so all the wine makers started, you know, making wines to his taste. Which is fascinating, right? It's,
Michael: But then you mentioned that since the advent of the World Wide Web, there's been that kind of a democratization of this process, that it's easier to find information and, you know, I wonder about that in light of these kinds of decision markets. You know, do you think that the web is improving our aggregate grasp on reality by flattening this kind of thing?
But yeah. But at the same time, there are other ways that get discussed in this book that the web is leading to just remarkable exponential kind of inequalities as well.
Matthew: Yeah. I mean, I think as you point out, that the web brings good and bad things with it. So the one, you've got powerful search engines.
We've got this immense connectivity. Anybody can basically put out their own wine newsletter and distribute it very cheaply, in the sense that you put up your own scores on the web and anybody can find them. So it does democratize things. But along with the sort of cheapening of the cost of conveying information and posting things, comes this difficulty that a lot of it is algorithm driven. It’s algorithmic. And those algorithms look for things we like and try to connect us to thinks we like, and they themselves can be built off of trying to connect you with things they think are popular. So the kind of exponential thing we talked about before, where people get connected to it, the more connects the algorithms can do that themselves, right? Because they push up things that are popular and sort of make Top 10 lists and put in popular things. And that can cause a feedback. It can also help us search for people that look a lot like us. So there's a lot of the book that is devoted to this idea of homophily, where people connect with other people who are very similar to themselves and algorithms can really make it much easier to find people who have exactly the same views that you have and exactly the same perspective and exactly the same background. And that's dangerous in some ways.
Michael: You bring this up in examining the double-edged sword that having high betweenness centrality, for example, gives you this sort of enormous political power. But in another sense, it exposes you and the entire network to new kinds of risk. And I feel like this is a fine point to dive into your chapter on, on finance and the 2008 crash and how these things came about. In general, there’s a deep question that I want to kind of orbit here, which is, how do we implement ways to balance the potent power of networks and of scaling networks with the new challenges that they issue us?
Matthew: Yeah. And I guess financial networks are a fascinating example because when you look at globalization, the amount of trade that we've seen in the world since the second world war has gone up by a factor of four, and financial integration has become worldwide. So right now, you know, if the markets sneeze in China, we know about it the next, next morning in New York. And I think that kind of an interconnectivity is very valuable in terms of helping investment flow to places that need it. And, it's actually been amazingly prosperous for a lot of countries. But at the same time, it means that now we've got this very well-interconnected system, and we saw the, some of the ramifications of that in 2008 where, you know, if we have a few companies that have over invested in in the wrong things — in that case it was subprime mortgages and some other mortgages and various loans — if they've all heavily invested in the same thing at the same time and those investments go sour, that can actually cause a pretty widespread contagion very rapidly. And luckily, we avoided complete catastrophe in terms of a total systemic meltdown, but it was dangerously close, in many ways.
Michael: You know, I was in reading this chapter, thinking a lot about the conversation I have with Jennifer Dunne about trophic networks, and you know, her, her conversation about, you know, we tend to think of this in a rather simple way that there's, you know, a keystone species like the Orca. That it, you know, were to be removed from the system, that's “too big to fail,” that would lead to this cascading collapse, but that what they found in this other sense was that the more nodes of these little species that, you know, the ones that we don't consider, that we pull out at the network, you thin the network by like 20% and that leads to cascading extinctions also.
And so growing up when I did, I tend to be kind of bitter about the bank bailouts…reading this it makes a lot of sense. Why we're just in this position now of dependency on systems that, are in this sort of unpleasant tension with the individual and with smaller institutions. But is there a sense in which, economists are talking about, the “too small and numerous to fail”? Because a big piece of this when you talk about with respect to news, especially the way that economics of scale…like a great example doesn't come up from the book is the way that Amazon has sort of sucked all the air out of the room for retail. And so at what point is it too centralized and too condensed in one space, that that becomes its own kind of risk?
Matthew: Right. I mean, I guess part of the financial system that we're sort of stuck with is the fact that there's two things that are very valuable.
One is economies of scale. So actually being a larger enterprise means that you can do different kinds of business and you can be one stop shopping for companies that can come in and get their foreign exchange, and they can do some hedging and they can get loans.
So there are values both in a scope and scale of being large and having these large enterprises. And then it's also valuable to have them all very well interconnected because they need to be moving money where the investments are and where things are or are going. And so you end up with a highly connected system.
But with lots of nodes, and the nodes tend to be fairly large. So even this sort of smaller ones that you're talking about nowadays are, we're talking billions of dollars, right? So there’s tens of thousands of nodes that are worth billions of dollars and they're all interconnected.
And I think that. Part of the real difficulties in managing the system becomes that it's no longer a simple market, where the sort of supply and demand works in the usual sense because now if somebody defaults in one part of the system, that actually can cause defaults for other, right? If I borrow a lot from you and I owe you a bunch. I don't make that payment. Now you can't make your payments. And that goes on. And we can have cascades in that sense. And that was sort of the real crux of the matter. And, in 2008, there were a series of different enterprises that owed money that couldn't of Lehman Brothers was sort of the poster child of it, but AIG, Fannie Mae and Freddie Mac, there were a bunch of people who had large debts that were unable to make payments on those. And that's where we end up now having a danger. And somehow we have to think about how do we both allow these markets to work, but make sure that they're safeguarded.
So you want to step in. You do want some regulation, but you don't want to regulate them out of existence. And that's the hard part, right, is figuring out the right level and how you do this, especially when it's a big complex system.
Michael: So in here you compare economic regulation to brain surgery, which I found to be a very appropriate kind of complexity science statement, right?
The challenge there, I think that the reason that that particular analogy is so rich is. As you say, you know, we really don't have the brain very well mapped out. Like, I mean, at one level of detail we do. But in terms of really understanding all of the functional relationships and even the ways that regions of the brain are communicating with one another, I mean, there's all these questions about, is photonic or acoustic communication going on? And so they're, you know, it may be that there are kinds of centrality that are not even being measured in the system.
And the question of how did we get a better look at the economy when we already have such strong trends for institutions to keep their information proprietary? Like we're actively working against the system in our attempts to understand it, in some respects.
Matthew: Yeah. And I think it’s a very strong analogy in the sense that, right now, scientists are basically mapping out the brain. And if you go and look at what the main concern of most central banks are, go to the Federal Reserve Bank.
I’ve talked to people at the Bank of England. You can talk to people at the Bank of France, the Bank of Mexico, and so forth. One of the main things they're trying to do is just map out the system. Really just get ahold of who's really connected to whom. And as you say, the companies don't want to give that information out because it's their proprietary information.
They don't want you to know what their trades are, who they're trading with, what their positions are. And the also the people who are regulating it have a certain scope. So one of the main things they're concerned about is what's known as shadow banking. So we have these large banks, investment banks that are actually regulated and have to make certain kinds of reports and a lot of their dealings you can see. There’s a lot of other institutions that aren't that different than banks — large insurance companies and other people who can actually, issue shares and help you get loans and do different things that aren't regulated in the same way — and the more you start regulating one part of the system, the more the other part of the system grows.
So,I always think of this as like whack-a-mole. I don't know if you know that game where every time something pops up and you, you think you've got that part of the system. Then basically the system morphs and people start putting their money elsewhere. You start regulating banks and savings and loans pop up. You start regulating savings and loans and people, you know, investment banks step up. Then hedge funds come and, it just keeps moving and it's a very difficult and involving system.
Michael: On top of this, it occurred to me that, when, when we talk about playing with other people's money in terms of our inability to accurately measure the externalities involved in these kinds of decisions, it's a massive blind spot in terms of what the actual transaction that's happening really is. And, it occurred to me that because of the sort of primitive nature of our early 21st Century accounting techniques, there's the sense in which, isn't every transaction playing with other people's money? That until we actually know where the values that we should be measuring resides, we're really kind of just splashing around and this in this brain and you know, God only knows the consequences,
Matthew: Right. And when you cut something off in one place and it actually has consequences far away because if you follow the stream back in terms of who owed money to whom, it can be four or five steps removed, and the consequences can be pretty, pretty broad.
And I think in general, you know, that part of the system, when you look at the systemic failures, it's been that, the people making the decisions don’t have the risk involved. It's not their personal risk.
Michael: Or it doesn’t seem to be. There's that question, which is kind of a kind of a Long Now Foundation question: how do we use regulation, how do we use financial incentive…like you talked about with the vaccinations, you know, how do you get people to care about the longer timescale and the bigger network portrait they're implicated in ?
Matthew: Yeah. I think it's very difficult than the financial system because it's so diffuse and so, so interconnected. I think the main thing at this point is really just getting better measurement, and then trying to have a little more advance warning of when people are making really poor decisions.
There were years of investments in very similar portfolios by lots of banks and were heavily weighted and leveraged in very narrow investments. That's something that just shouldn't happen. That's easy to identify, but you have to be able to see it. You know, that's like a large tumor growing in the brain and not seeing it. That's not major brain surgery. That's minor. So I think there's some things that, that we can do much better at. And then there's other parts that are going to be more complicated and difficult to deal with in the long run.
Michael: There's another link here I think. In your discussion about informal relational networks and romantic relational networks…to get into this thing about the clustering coefficient and embeddedness and dispersion. There's something about how do we motivate people to make decisions for each other and not just put themselves — you know, even though it, those two things are obviously kind of inextricable. It seems very intimately related to this question of, “how likely is this particular couple to stay together?” What do you think about that?
Matthew: Yeah, yeah. There's a fun little paper, I'm trying to remember who the authors were, but Jon Kleinberg was one of the authors, and they looked at, at, people's Facebook connections and they were trying to figure out who were spouses and who weren't. So you just look at the network. And so I look at a particular person and I try and figure out who's that person's spouse and the kind of thing, which really tells you how to identify that person is that you end up having a lot of friends in common, but a lot of friends in common across different spheres.
So your work friends become known to each other. Your hobby friends, your high school friends, you meet people through all these different things. But if you're very close to somebody, you meet them on many different levels. And so they were able to actually, with high accuracy, point out who somebody's significant other spouse was. And in the cases where it didn't, when they looked back some time later, it turns out that the person, often they'd broken up, right? So when it didn't match their score, in terms of identifying these people, they often turned out that there was a reason for that.
Michael: Yeah. It seems as though that, the ability to determine that embeddedness and that dispersiveness of our social networks, again, is kind of an indicator of a good faith. And you know, you talk a lot about the way that, preferential attachment emerges in networks like job referrals.
You know, I've gone out to Burning Man a number of times, and there's a kind of very interesting economic conversation going out there with respect to revealing the ways in which gift economies exists in the rest of the world, also. That we've been sort of blind to the ways that our families and our friendships do not rely on this sort of first layer conscious kind of transactional thinking. I mean, obviously at some level you can exhaust the trust with your own family and it becomes like tit for tat. But you know, this question of humanizing the global economy seems to have a lot to do with, again just revealing the ways in which we are leaning on one another and all of these informal ways.
Matthew: Yeah. I mean, there's two things that are probably important to for us as humans to recognize. One is that we, we often form relationships for one reason, but they end up serving lots of purposes.
Right? So I ended up, hanging out with my colleagues, but I'll end up getting my advice from them and hearing a lot of information from them and other things that weren't the original intended purpose, but also that what we do ends up impacting lots of people in a different way.
All of our actions have network consequences. And I think that's where networks get interesting, right? Is the sort of externalities, the fact that if I learn, you know, I spend a lot of time learning some new programming, I can help my friends by spreading that knowledge. And so my acquisition of skills is something that's valuable to other people. And my acquisition of knowledge in general is something that's useful for my whole community. And we don't often think that way. We often think, do I want to learn this? Not do I want to learn this for my community?
And those are, those are two different questions. And the second one is what's actually happening in the first one, is the way we often make decisions.
Michael: Yeah. I love the, the frequency with which I'm noticing people talk about ikigai. You know, what you're good at, what you enjoy doing, what the world wants, and what you can get paid for.
And I wonder, to the degree that we, you know, like for example, my friends in New York city have the most bizarre, specific jobs that could in no way ever be supported by a city of one 10th the size. And, you know, It raises questions for me about whether the increased multidimensionality of our accounting and the scale of the networks in which we're participating are affording us the opportunity to reach ikigai, to enact it more frequently.
Matthew: So there's something that's known as multiplexing, which is interesting, which is an multiplexing is a term that sociologists refer to as sort of layering our networks on top of each other.
Right? So I've, I've got my, whoever is helping me out on day to day things, loans, and maybe they're giving me some kind of help. Then there's other people that are giving me advice. There's people who I work with. There's people who I rely on for medical help. There's all kinds of different things that we do. And in small scale societies, traditionally those things are layered very closely to each other. So I interact with the same people for all purposes. And then when you get into larger and larger scale societies, those things can begin to diverge.
And New York is probably an extreme example of that, right? Where people can really specialize, not just in what they do, but they can be talking to very different people for different purposes. And that allows that specialization, right? So that I go to one person for very particular kinds of information and I go for somebody else for other kinds of things in a small village that it might be the same person that I'm transacting with who is also giving me, you know, my mental health advice.
And that's not necessarily a good thing. Right? And so I think these larger scale societies allow you to, to sort of disentangle those relationships and specialize a bit more.
Michael: And yet, we've been at this point for over a century where we have trouble with the scale of modern life, even comprehending it. I'd like to open this piece up to the fake news problem and how networks do not just inform and empower us, but they also can mislead us or lead us into making, decisions that are ultimately maladaptive.
Matthew: Right. So, yeah.
Yeah. I mean, I think, you know, one of the fascinating things about humans, and there's a nice book by Joe Henrich, this sort of talks about what's special about humans. And I think one thing that's special about humans is that we were able to grasp concepts in the abstract. So lots of species can teach their young, they can help each other. They do all kinds of things that humans do. They communicate. But what we do that's very different is I can tell you something like, you know, I was just in China last week, I could say, and I could explain a city to you that I'd been to, and you maybe have never been there, but you could begin to imagine it and see what it's like.
And so humans have this ability to communicate things in the abstract and then imagine them and store them. That also has makes us susceptible to superstitions, to false beliefs. I can tell you something that's not true, and you can imagine it and believe it, and if it's a coherent story, then it's something that you can begin to grasp onto. And when we combine that with the network structures where we're hearing things from maybe the same people or hearing them over and over again, and we have the susceptibility to believe things that aren't necessarily true. That opens us up to lots of difficulties in terms of what's truth and where is it residing in a network.
Michael: So there's a kind of a backflow or reflux that I see going on that's related to Alvin and Marie Toffler’s Future Shock and people pulling out of systems, retreating to sort of fundamentalist communes or the rise of nationalist extremism that, I think is pretty much on everyone’s mind these days.
And you know, I wonder is in a way at least it seems a local optimum to extract oneself from a network that you realize is exposing you to these kinds of risks. That in a way at the, in a sort of local sense, it seems like pulling out of the system is actually adaptive. Science fiction author, Charles Stross has this really interesting thought experiment in his book Glass House that humankind has gone fully digital.
And so you're, you're backed up on a server somewhere and guaranteed immortality. But what this does is it exposes everyone to the threat of being hacked. You know? And so this, this was a book written before the internet of things, but I think that this gets into these kinds of questions of, “Do I or do I not put in a smart pacemaker?,” are very real difficult questions.
Matthew: Right. Yeah. I mean, I think one thing that I take away from my studies and networks is just an awareness of how insular our networks tend to be and how we don't realize how homophilistic the world is and how much we tend to be talking to other people who are similar to ourselves.
We don't realize how much over-weighting we can be or how much weight we can be placing on a single individual, either directly or indirectly in terms of of their influence on us. And I think, you know, just asking simple things we can do every day are ask, when somebody tells us something, ask more about that source, right?
Where, where did they really hear that? Where did that information come from? Why did they come to that belief? And those kinds of questions can really be illuminating. And also putting yourself in completely uncomfortable situations. I always think it's very valuable, right? So going places you would never normally go talk to people that you wouldn't normally talk to.
People walk into a room and you can see it at a large conference or something. People naturally gravitate towards the people who are of their same subfield and their same whatever. They see a friend and they zoom right to that. It's not that they go out and meet new people and expand their horizons, it's that they're sort of reinforcing their smaller communities. And that's something that's natural for us. But it could be very confining.
Michael: You bring this up with the Schelling segregation. For those who aren't familiar with this, the model suggests that all you need is to not want to be in a very, very small minority that leads to like these complete segregation of neighborhoods.
So that kind of begs the question, “What evolutionary forces incentivize this kind of exploratory aisle-crossing and curious and recombinant kind of behavior?” So, we know that cities kind of do this. That cities as social reactors encourage active mixing, right? How do you see this being deployed in economic regulation and these kind of areas, or how do you imagine it could be deployed?
Matthew: I think it's very difficult actually. So, you know, one area that was fascinating was when you looked at, if you go back to the 1970s and 1980s, there was a push by a lot of school districts to try and build more ethnically balanced schools.
And they worked hard to do that. And what they ended up building was the larger schools that then could allow them to bring people together from different areas. Sometimes it was busing, sometimes it was just putting together different school districts. And they built these larger things that on paper were very well balanced.
But then when you looked inside the schools, they became the most segregated schools because then the friendship patterns inside — you had these large enough groups that it all split up. And so it's a really difficult thing to do. So even when you build large cities, you know, the cities end up with their neighborhoods and their structures internally. It's not an easy problem. And I think part of it is just people have to be conscious of it. And you know, you have to sort of encourage… At first, I always thought, there's a lot of funding available as a scientist for interdisciplinary work, and you always think, why are they just promoting it? Do they really know what they're promoting? They just want to put people together, and so forth. But I think we under-explore in that sense, right?
Because on a day to day basis, we're often rewarded and it's easy for us to feel comfortable around people. It's just more pleasant. Everything on a day to day basis, being in your own comfortable niche where you know everybody, you know what they're going to say, you understand what they're talking about and so forth. Rather than going out and suddenly feeling like an idiot and not understanding anything that's going on and being in a place that really makes you feel uneasy.
That's hard. But it's valuable. Right? So it's difficult.
Michael: This is sort of related to the question of…in reading your section on accounting at the macroeconomic scale, this seems like one of the great promises to me of the blockchain, right? That we have this network level view that we can use this network level view to tokenize previously invisible forms of value, to make certain kinds of economic activity or human activity, human effort, to bring it into the labor portrait.
But then again, like…we’ve been touching on it a lot. There’s this sense, like Kevin Kelly talks about the expansion of ignorance, like the more you know, the more you know what you're doing.
Matthew: Right, right.
Michael: And so, what do you see as the sort of emergent problems or emergent risks of having a better portrait of what's actually going on?
Matthew: Yeah, and I guess it's always hard to think that better and more information is dangerous. I think here, having a better view of the fuller world and how our communication is taking place. I think it'll help us understand a lot of the polarization that's going on. And you know, there's sort of two main forces that are changing in the world right now and that are leading to a world that's much more fractional.
At one level it's this connectivity which allow us things to spread very easily and to sort of take root. And the other is this tendency of us to sort of separate ourselves. And at the same time, there's a bunch of fundamental economic forces that are leading to inequality, which sort of push societies to have more nationalistic views and the rise of populism. So it's a difficult situation, but I think being aware of these things allows us to understand some difficulties that are much more structural and social, and not just plain economics. So there's always a tendency when we see economic problems to say, “Okay, well let's put money on it. We'll tax this, we'll subsidize this, we'll move money around. You know, for inequality, we should be moving money here and there.”
But these are deep structural problems that are leading to a lot of this stuff. And if we understand those structural problems and where the networks come into play, that leads to a very different prescription for sort of long-term health. Don't just treat the symptoms, these local problems that we're having, but, you know, start dealing with these structural issues, which are really at the heart of a lot of the sort of long-term societal problems that we face.
Michael: You talk about Moving To Opportunity as this seems like a really great example. Care to talk about that a little?
Matthew: Sure, yeah. Moving To Opportunity was a fascinating study that the US government did in the 90s and there what they did is they took families and they had control group that they didn't do anything with. And then they had two different groups.
One, they gave vouchers for housing and they said, “We'll pay for your housing, but you have to move to a wealthier neighborhood.” So they were taking people from the bottom of the income distribution and they said, “You have to move to this other neighborhood in order to get paid.” And then there was another group that they said, “We'll just pay for your housing, but you can stay where you are.”
And then, you know, fast forward 20 years and people, now there's a series of papers that have looked at the, outcomes of that. And there's a paper by a Raj Chetty, Nathan Hendron, and Larry Katz, that that sort of mapped out, say, take an eight year old who was moved from one of the poor neighborhoods to one of the wealthier neighborhoods, and look at what their lifetime earnings impact of that move is.
Look at an eight year old to stay behind. And then one who moved has about $300,000 extra expected lifetime earnings, which is nontrivial. I mean, this is a . They have all kinds of measures. Health is better, lower rates of incarceration, teenage pregnancy goes down.
So there's all kinds of benefits from just taking somebody out of one neighborhood and moving them to another neighborhood. And that gives us a powerful measure of how important that community is in terms of affording you the opportunities to sort of advance and to get educated and to get employment and to avoid a lot of troubles.
But that's not easy to do, right? We can't socially engineer the whole world where we start moving everybody around, but it points out how powerful the network structure is in terms of offering you those opportunities.
Michael: You know, that that kind of mixing actually seems to be an effect of the internet, like time and time again, looking at aggregate decision making or the way that information diffuses…you've got all these great examples in here. Like taking the estimate weight of the ox, and how you want one of those degree models on this. And over time, everyone kind of evens out. And I looked at that and I saw the way that the so-called developing world has really come up at the same time that the, the priority and dominance of some of the first world nations, like the United States has really been challenged by the rise of the World Wide Web.
And so you joke in here about our Star Trek future. But I think, you know, there's a sense in which, in spite of the rising inequality that we see when we cut this in certain ways, there's another sense in which it does seem as though the baseline, the eradication of extreme poverty and so on, is really going up.
And so I'm curious, immersing yourself in this, as you do, where do you stand? How hopeful are you? What do you consider the most sort of optimistic revelations of this kind of research?
Matthew: I mean, I think the most amazing things that I found looking at these were how important international trade and globalization has been, in two ways. One is, you've sort of mentioned a few. If you look at world poverty rates, they've fallen fairly dramatically, and there's still an enormous amount of work to be done. And the rates that they call poverty rates are really low. And so it's not a high benchmark, but that's certainly made a big difference. And if you go to China now compared to China 30 years ago, I mean, it's an enormously wealthier nation and many more people are living prosperous lives. So that's wonderful.
And then on the other hand, you also get another benefit, which has been peace. And I think this is something we really under-appreciate.
I did a study with Stephen Nye where we looked at the levels of conflict over time and level of conflict have fallen by a factor of 10. If look at wars between the Napoleonic War period up through the Second World War, countries were just routinely at war. It was just a sort of a constant fact of life.
And if you look since then, it’s very rare for countries that trade with each other to be at war. And so that's brought an immense peace to the world. And so, you know, the interconnectivity, and that's really a trade network, and it's a very dense network, and you can look at it and it's not nuclear weapons, it's not democracies. You can control for all these things. It's really the trade that that ends up [doing it]. It's very difficult to find anything else mattering. So I think you can be really optimistic on a lot of grounds in terms of how the interconnectedness is helping the world. And then it's, you're seeing it on the web now.
I've been teaching courses on Coursera. People all around the world can beat learning game theory, right? For me, I teach a course on networks and people all around the world come up to me and say, “I took your course on networks. There wasn’t anything at my university on this.” And, you know, that's something that's really an evener. And so you see great hope from that.
The difficulties are that this also allows us to do other things and there's always good and bad that comes with any kind of technological advance.
Michael: Yeah, you mention — this is on page two 33 of the hardcover — you mention, “If one wants a recipe for lowering the incidence of wars in Africa, in the Middle East, the message is clear: grow the economies and the regional trade networks and especially promote trade between potential adversaries.”
And so again, we're back to this question of. How do we encourage the mixing, you know, Nelson Mandela working with apartheid and getting people into these reconciliatory conversations. What is that gonna take in a world as polarized as the world that we have.
Matthew: Yeah. I think, you know, part of it is when you look at diplomacy, diplomacy is still very much thought of as a, a political thing where people are trying to write contracts and write alliance papers and sorta, but somehow, unless those things are really embedded in longterm trade/development issues, you won't see a permanent change. So, if you look in the Middle East and you look at the countries that are there, basically, if you look at Israel, you can't find any of its direct neighbors, which are in the top 10 of its trade partners. Its trade partners are all Europe, America… You know, it's not trading locally. It's trading out to the rest of the world. And as long as that keeps working that that means you're going to have conflict.
You know, the neighbors, they’re not trading with each other. They see each other as adversaries and competitors, not as partners. And it's very difficult to change that, for lots of reasons. But until that's changed, we're not going to see a change, I don't think, in sort of long-term peace in that region.
Michael: It’s interesting listening to you talk about this now, it reminds me of my experience as a musician in Austin, Texas, right? Where as the real estate values went up, major venues started closing and artists started looking outside of the community for their, for work, you know?
And so artists were gone, most of the year on tour, and when they came home, they were in this very adversarial relationship with this emerging new class of like software programmers and venture capitalists that had come in, and real estate agents. And suddenly the town is cut down the middle by this, this tension between what historically has given it its identity and what is now really causing it to grow and thrive in the way that it is.
And so this is, you know, these are problems that I think are are really ubiquitous and universal problems. This is not just like geopolitical stuff. This is something that I think all of us can identify at work in our own lives.
I'm curious just to kind of tie a bow on this, I'm curious to move beyond the book. What are you working on now? And then also what do you see as some of the most promising avenues that this research is taking, both in your own work and under the work of your colleagues?
Matthew: Sure. So two things I'm working on now that I really excited about.
One is developing real measures of social capital to try and understand what it is that helps people, as we talked about the Moving To Opportunity.
So can we put numbers on those? Can, can we really figure out what it is that affects a person? Is it knowledge? Is it just, if I want somebody to understand education and then understand how they can improve their lives, what kinds of information do they need? Or is it access? Is it that they have to have doors open for them and they have to have an ability to have a friend have done something to be able to pull them into that.
So that's one area and there I think we can see very strong notions of social capital and the importance of that and getting education and so forth. The other is something we talked about in terms of financial and other kinds of networks. And I think increasingly, we’re able to start mapping out those kinds of networks and look at how economies work, trade flows, financial flows, and really understand how it is that changes in one part of an economy ripple through.
And the better the mapping, you know, again this, it looks like mapping the brain out is sort of, you know, we're at that mapping part and the better those maps get, the better we'll understand how to begin to intervene and to begin to make the system stronger and better because it's evolving on its own and not necessarily always in good ways. A lot of things happen. We know that the growth and globalization we've been talking about, a lot of these things are wonderful, but the system sort of on on its own trajectory in the question is, can we understand that trajectory and and can we nudge it sometimes in a little better direction?
Michael: What do you suppose would be the takeaway. Assuming nothing about whoever's listening to this show, what do you think would be some of the most empowering advice that you could offer people in terms of understanding these kinds of systems in our place and the systems? That people can use to increase their personal mobility and their opportunity and so on.?
Matthew: I guess that one piece of, of putting people, put yourself in an uncomfortable situation. And I think by far the best personal advice you can come up with out of these things because it's easy to underestimate the power of our social structures and how constraining they are.
So our lives, we think of as all our decisions, and we've made all these choices in our lives, but all those choices were made in a pretty narrow set of options. It's not as if we had every possible choice we could make. And understanding that is really critical. And so somehow if we can expand our horizons and expand those sets of options, that's where real, I dunno, enlightenment comes from, I think in terms of, you know, better understanding the world.
That's a hard thing to do though.
Michael: Wonderful. Matthew, thank you so much for talking with us.
Matthew: Thanks, Mike. It's been wonderful.