Articles, Blog

Nicholas Christakis: How social networks predict epidemics

December 12, 2019


For the last 10 years, I’ve been spending my time trying to figure out how and why human beings assemble themselves into social networks. And the kind of social network I’m talking about is not the recent online variety, but rather, the kind of social networks that human beings have been assembling for hundreds of thousands of years, ever since we emerged from the African savannah. So, I form friendships and co-worker and sibling and relative relationships with other people who in turn have similar relationships with other people. And this spreads on out endlessly into a distance. And you get a network that looks like this. Every dot is a person. Every line between them is a relationship between two people — different kinds of relationships. And you can get this kind of vast fabric of humanity, in which we’re all embedded. And my colleague, James Fowler and I have been studying for quite sometime what are the mathematical, social, biological and psychological rules that govern how these networks are assembled and what are the similar rules that govern how they operate, how they affect our lives. But recently, we’ve been wondering whether it might be possible to take advantage of this insight, to actually find ways to improve the world, to do something better, to actually fix things, not just understand things. So one of the first things we thought we would tackle would be how we go about predicting epidemics. And the current state of the art in predicting an epidemic — if you’re the CDC or some other national body — is to sit in the middle where you are and collect data from physicians and laboratories in the field that report the prevalence or the incidence of certain conditions. So, so and so patients have been diagnosed with something, or other patients have been diagnosed, and all these data are fed into a central repository, with some delay. And if everything goes smoothly, one to two weeks from now you’ll know where the epidemic was today. And actually, about a year or so ago, there was this promulgation of the idea of Google Flu Trends, with respect to the flu, where by looking at people’s searching behavior today, we could know where the flu — what the status of the epidemic was today, what’s the prevalence of the epidemic today. But what I’d like to show you today is a means by which we might get not just rapid warning about an epidemic, but also actually early detection of an epidemic. And, in fact, this idea can be used not just to predict epidemics of germs, but also to predict epidemics of all sorts of kinds. For example, anything that spreads by a form of social contagion could be understood in this way, from abstract ideas on the left like patriotism, or altruism, or religion to practices like dieting behavior, or book purchasing, or drinking, or bicycle-helmet [and] other safety practices, or products that people might buy, purchases of electronic goods, anything in which there’s kind of an interpersonal spread. A kind of a diffusion of innovation could be understood and predicted by the mechanism I’m going to show you now. So, as all of you probably know, the classic way of thinking about this is the diffusion-of-innovation, or the adoption curve. So here on the Y-axis, we have the percent of the people affected, and on the X-axis, we have time. And at the very beginning, not too many people are affected, and you get this classic sigmoidal, or S-shaped, curve. And the reason for this shape is that at the very beginning, let’s say one or two people are infected, or affected by the thing and then they affect, or infect, two people, who in turn affect four, eight, 16 and so forth, and you get the epidemic growth phase of the curve. And eventually, you saturate the population. There are fewer and fewer people who are still available that you might infect, and then you get the plateau of the curve, and you get this classic sigmoidal curve. And this holds for germs, ideas, product adoption, behaviors, and the like. But things don’t just diffuse in human populations at random. They actually diffuse through networks. Because, as I said, we live our lives in networks, and these networks have a particular kind of a structure. Now if you look at a network like this — this is 105 people. And the lines represent — the dots are the people, and the lines represent friendship relationships. You might see that people occupy different locations within the network. And there are different kinds of relationships between the people. You could have friendship relationships, sibling relationships, spousal relationships, co-worker relationships, neighbor relationships and the like. And different sorts of things spread across different sorts of ties. For instance, sexually transmitted diseases will spread across sexual ties. Or, for instance, people’s smoking behavior might be influenced by their friends. Or their altruistic or their charitable giving behavior might be influenced by their coworkers, or by their neighbors. But not all positions in the network are the same. So if you look at this, you might immediately grasp that different people have different numbers of connections. Some people have one connection, some have two, some have six, some have 10 connections. And this is called the “degree” of a node, or the number of connections that a node has. But in addition, there’s something else. So, if you look at nodes A and B, they both have six connections. But if you can see this image [of the network] from a bird’s eye view, you can appreciate that there’s something very different about nodes A and B. So, let me ask you this — I can cultivate this intuition by asking a question — who would you rather be if a deadly germ was spreading through the network, A or B? (Audience: B.) Nicholas Christakis: B, it’s obvious. B is located on the edge of the network. Now, who would you rather be if a juicy piece of gossip were spreading through the network? A. And you have an immediate appreciation that A is going to be more likely to get the thing that’s spreading and to get it sooner by virtue of their structural location within the network. A, in fact, is more central, and this can be formalized mathematically. So, if we want to track something that was spreading through a network, what we ideally would like to do is to set up sensors on the central individuals within the network, including node A, monitor those people that are right there in the middle of the network, and somehow get an early detection of whatever it is that is spreading through the network. So if you saw them contract a germ or a piece of information, you would know that, soon enough, everybody was about to contract this germ or this piece of information. And this would be much better than monitoring six randomly chosen people, without reference to the structure of the population. And in fact, if you could do that, what you would see is something like this. On the left-hand panel, again, we have the S-shaped curve of adoption. In the dotted red line, we show what the adoption would be in the random people, and in the left-hand line, shifted to the left, we show what the adoption would be in the central individuals within the network. On the Y-axis is the cumulative instances of contagion, and on the X-axis is the time. And on the right-hand side, we show the same data, but here with daily incidence. And what we show here is — like, here — very few people are affected, more and more and more and up to here, and here’s the peak of the epidemic. But shifted to the left is what’s occurring in the central individuals. And this difference in time between the two is the early detection, the early warning we can get, about an impending epidemic in the human population. The problem, however, is that mapping human social networks is not always possible. It can be expensive, not feasible, unethical, or, frankly, just not possible to do such a thing. So, how can we figure out who the central people are in a network without actually mapping the network? What we came up with was an idea to exploit an old fact, or a known fact, about social networks, which goes like this: Do you know that your friends have more friends than you do? Your friends have more friends than you do, and this is known as the friendship paradox. Imagine a very popular person in the social network — like a party host who has hundreds of friends — and a misanthrope who has just one friend, and you pick someone at random from the population; they were much more likely to know the party host. And if they nominate the party host as their friend, that party host has a hundred friends, therefore, has more friends than they do. And this, in essence, is what’s known as the friendship paradox. The friends of randomly chosen people have higher degree, and are more central than the random people themselves. And you can get an intuitive appreciation for this if you imagine just the people at the perimeter of the network. If you pick this person, the only friend they have to nominate is this person, who, by construction, must have at least two and typically more friends. And that happens at every peripheral node. And in fact, it happens throughout the network as you move in, everyone you pick, when they nominate a random — when a random person nominates a friend of theirs, you move closer to the center of the network. So, we thought we would exploit this idea in order to study whether we could predict phenomena within networks. Because now, with this idea we can take a random sample of people, have them nominate their friends, those friends would be more central, and we could do this without having to map the network. And we tested this idea with an outbreak of H1N1 flu at Harvard College in the fall and winter of 2009, just a few months ago. We took 1,300 randomly selected undergraduates, we had them nominate their friends, and we followed both the random students and their friends daily in time to see whether or not they had the flu epidemic. And we did this passively by looking at whether or not they’d gone to university health services. And also, we had them [actively] email us a couple of times a week. Exactly what we predicted happened. So the random group is in the red line. The epidemic in the friends group has shifted to the left, over here. And the difference in the two is 16 days. By monitoring the friends group, we could get 16 days advance warning of an impending epidemic in this human population. Now, in addition to that, if you were an analyst who was trying to study an epidemic or to predict the adoption of a product, for example, what you could do is you could pick a random sample of the population, also have them nominate their friends and follow the friends and follow both the randoms and the friends. Among the friends, the first evidence you saw of a blip above zero in adoption of the innovation, for example, would be evidence of an impending epidemic. Or you could see the first time the two curves diverged, as shown on the left. When did the randoms — when did the friends take off and leave the randoms, and [when did] their curve start shifting? And that, as indicated by the white line, occurred 46 days before the peak of the epidemic. So this would be a technique whereby we could get more than a month-and-a-half warning about a flu epidemic in a particular population. I should say that how far advanced a notice one might get about something depends on a host of factors. It could depend on the nature of the pathogen — different pathogens, using this technique, you’d get different warning — or other phenomena that are spreading, or frankly, on the structure of the human network. Now in our case, although it wasn’t necessary, we could also actually map the network of the students. So, this is a map of 714 students and their friendship ties. And in a minute now, I’m going to put this map into motion. We’re going to take daily cuts through the network for 120 days. The red dots are going to be cases of the flu, and the yellow dots are going to be friends of the people with the flu. And the size of the dots is going to be proportional to how many of their friends have the flu. So bigger dots mean more of your friends have the flu. And if you look at this image — here we are now in September the 13th — you’re going to see a few cases light up. You’re going to see kind of blooming of the flu in the middle. Here we are on October the 19th. The slope of the epidemic curve is approaching now, in November. Bang, bang, bang, bang, bang — you’re going to see lots of blooming in the middle, and then you’re going to see a sort of leveling off, fewer and fewer cases towards the end of December. And this type of a visualization can show that epidemics like this take root and affect central individuals first, before they affect others. Now, as I’ve been suggesting, this method is not restricted to germs, but actually to anything that spreads in populations. Information spreads in populations, norms can spread in populations, behaviors can spread in populations. And by behaviors, I can mean things like criminal behavior, or voting behavior, or health care behavior, like smoking, or vaccination, or product adoption, or other kinds of behaviors that relate to interpersonal influence. If I’m likely to do something that affects others around me, this technique can get early warning or early detection about the adoption within the population. The key thing is that for it to work, there has to be interpersonal influence. It cannot be because of some broadcast mechanism affecting everyone uniformly. Now the same insights can also be exploited — with respect to networks — can also be exploited in other ways, for example, in the use of targeting specific people for interventions. So, for example, most of you are probably familiar with the notion of herd immunity. So, if we have a population of a thousand people, and we want to make the population immune to a pathogen, we don’t have to immunize every single person. If we immunize 960 of them, it’s as if we had immunized a hundred [percent] of them. Because even if one or two of the non-immune people gets infected, there’s no one for them to infect. They are surrounded by immunized people. So 96 percent is as good as 100 percent. Well, some other scientists have estimated what would happen if you took a 30 percent random sample of these 1000 people, 300 people and immunized them. Would you get any population-level immunity? And the answer is no. But if you took this 30 percent, these 300 people and had them nominate their friends and took the same number of vaccine doses and vaccinated the friends of the 300 — the 300 friends — you can get the same level of herd immunity as if you had vaccinated 96 percent of the population at a much greater efficiency, with a strict budget constraint. And similar ideas can be used, for instance, to target distribution of things like bed nets in the developing world. If we could understand the structure of networks in villages, we could target to whom to give the interventions to foster these kinds of spreads. Or, frankly, for advertising with all kinds of products. If we could understand how to target, it could affect the efficiency of what we’re trying to achieve. And in fact, we can use data from all kinds of sources nowadays [to do this]. This is a map of eight million phone users in a European country. Every dot is a person, and every line represents a volume of calls between the people. And we can use such data, that’s being passively obtained, to map these whole countries and understand who is located where within the network. Without actually having to query them at all, we can get this kind of a structural insight. And other sources of information, as you’re no doubt aware are available about such features, from email interactions, online interactions, online social networks and so forth. And in fact, we are in the era of what I would call “massive-passive” data collection efforts. They’re all kinds of ways we can use massively collected data to create sensor networks to follow the population, understand what’s happening in the population, and intervene in the population for the better. Because these new technologies tell us not just who is talking to whom, but where everyone is, and what they’re thinking based on what they’re uploading on the Internet, and what they’re buying based on their purchases. And all this administrative data can be pulled together and processed to understand human behavior in a way we never could before. So, for example, we could use truckers’ purchases of fuel. So the truckers are just going about their business, and they’re buying fuel. And we see a blip up in the truckers’ purchases of fuel, and we know that a recession is about to end. Or we can monitor the velocity with which people are moving with their phones on a highway, and the phone company can see, as the velocity is slowing down, that there’s a traffic jam. And they can feed that information back to their subscribers, but only to their subscribers on the same highway located behind the traffic jam! Or we can monitor doctors prescribing behaviors, passively, and see how the diffusion of innovation with pharmaceuticals occurs within [networks of] doctors. Or again, we can monitor purchasing behavior in people and watch how these types of phenomena can diffuse within human populations. And there are three ways, I think, that these massive-passive data can be used. One is fully passive, like I just described — as in, for instance, the trucker example, where we don’t actually intervene in the population in any way. One is quasi-active, like the flu example I gave, where we get some people to nominate their friends and then passively monitor their friends — do they have the flu, or not? — and then get warning. Or another example would be, if you’re a phone company, you figure out who’s central in the network and you ask those people, “Look, will you just text us your fever every day? Just text us your temperature.” And collect vast amounts of information about people’s temperature, but from centrally located individuals. And be able, on a large scale, to monitor an impending epidemic with very minimal input from people. Or, finally, it can be more fully active — as I know subsequent speakers will also talk about today — where people might globally participate in wikis, or photographing, or monitoring elections, and upload information in a way that allows us to pool information in order to understand social processes and social phenomena. In fact, the availability of these data, I think, heralds a kind of new era of what I and others would like to call “computational social science.” It’s sort of like when Galileo invented — or, didn’t invent — came to use a telescope and could see the heavens in a new way, or Leeuwenhoek became aware of the microscope — or actually invented — and could see biology in a new way. But now we have access to these kinds of data that allow us to understand social processes and social phenomena in an entirely new way that was never before possible. And with this science, we can understand how exactly the whole comes to be greater than the sum of its parts. And actually, we can use these insights to improve society and improve human well-being. Thank you.

80 Comments

  • Reply QuijanoPhD September 16, 2010 at 11:01 pm

    Is it just me, or is this talk eerily similar to an almost identical talk that someone else gave a few months back?

  • Reply LudicrousTachyon September 16, 2010 at 11:06 pm

    @Ambushcrysis He's doing the same thing with much less interference. I'm more willing to give the name of one good friend than all of them as I would suspect something fishy. He's also saying that the method he is using is sufficient. I may have 100 friends on Facebook, but is it really necesary to ask me about them all to make the map maybe 10% more accurate?

  • Reply BBwoan September 16, 2010 at 11:27 pm

    @DrQuijano exactly what I felt

  • Reply GodofCider September 17, 2010 at 12:10 am

    @alphadawg44 And the food you eat, the clothing you wear, the building you're taking shelter in, and the computer you're communicating upon; not to mention the language you're using.

  • Reply Ambushcrysis September 17, 2010 at 12:17 am

    @LudicrousTachyon If you're already on a social networking site, then all your friends are already on display for everyone to see (Unless you have it hidden). So if someone created a program where all you had to do was go through your friends list and check off who you know in real life, and that information would automatically get sent to a database where it would then be compared to other people's friends, I'm sure the accuracy would be much much more than 10%.

  • Reply Umbalafum September 17, 2010 at 12:37 am

    @alphadawg44
    surely FoxNews is already on it.

  • Reply TalynCo September 17, 2010 at 2:07 am

    This sounds hugely intrusive.

  • Reply BlowDevilUp September 17, 2010 at 2:15 am

    I'm just not that interested in product adoption. Even if the product advertisement has been crafted especially for me. In fact I would rather isolate myself from any additional product advertisement altogether.

  • Reply Sebastian Brito September 17, 2010 at 2:24 am

    facebook works for something good! at last.

  • Reply HelplessVictim September 17, 2010 at 3:31 am

    @cGBaDKaRMa
    Economists MAKE A LIVING figuring out where people are, what they're thinking, and what they want to buy. This is nothing new.

  • Reply Pat H September 17, 2010 at 4:02 am

    this is dangerous within our current economic system; but it seems very revolutionary

  • Reply Oberonjames September 17, 2010 at 5:04 am

    This all sounds like a benevolent version of 1984….

  • Reply oicub2 September 17, 2010 at 5:30 am

    You've been Mapped 14:08
    You Are the ginnie pig

  • Reply TheIntrepid September 17, 2010 at 5:39 am

    I feel like I got the gist after about ten minutes

  • Reply Jonboy207 September 17, 2010 at 5:57 am

    Revolutionary. Amazing.

  • Reply Keisha Fabio September 17, 2010 at 6:27 am

    good thing not all people are affected by audio alone.. for some reason I find this presentation with a somewhat negative vibe, perhaps his body language, perhaps his choice of words/tone.. anyhow the idea is good & potent. would be interesting if it is implemented in a good way.

    regards,
    potentially paranoid person =]

  • Reply chernobila September 17, 2010 at 7:57 am

    this is very raw and therefore boring. Not there yet buddy. I ll check back

  • Reply CharBroiled04 September 17, 2010 at 9:56 am

    @chernobila

    I couldn't disagree more. He appears to have a very refined structure here. If he had given the theory and no example studies, I'd agree… but he quoted several already completed studies.

  • Reply Abel Culp September 17, 2010 at 10:20 am

    Maybe we can collect information about centrally located people to eliminated political discent. Edward Burnays would love computational social science to manufacture consent. With propaganda and mapping the government would only need to control 20 to 30 %, to control the larger society. That is why totalitarian governments like East Germany kept dossiers on individuals of interest. Does this scare anyone else?

  • Reply brian cooper September 17, 2010 at 11:06 am

    The intimation you give away without even thinking about it. Let's hope its used for good

  • Reply amjPeace September 17, 2010 at 11:10 am

    Kevin Bacon is at the very center!

  • Reply roidroid September 17, 2010 at 12:19 pm

    @amjPeace Kevin Bacon, in the Total Perspective Vortex.

  • Reply redsbr September 17, 2010 at 12:45 pm

    9:53 "or to predict the adoption of a product.." did he just say that?

    I mean, why don't you just start an epidemic WHILE predicting the adoption of a vaccine youve also created for it.. you know…. since money and power is the only thing corperations care about.

  • Reply Eyal Lev September 17, 2010 at 12:59 pm

    sounds like a good idea. say you want to know if/when a population might turn towords being commuiniest, so you gather a few random ppl, ask them if they are commuiniest, and than ask then to name a friend to be questioned.

  • Reply redsbr September 17, 2010 at 1:34 pm

    Just know you cant quanitify Emotion, only the behavior you observe (or take the liberty of acquiring) from people. Oh and speaking of emotions, we hate you.

  • Reply darksean99 September 17, 2010 at 3:37 pm

    @urcritic I see no reason why it can't be used for both.

  • Reply AutodidacticPhd September 17, 2010 at 5:42 pm

    @Ikkath And how do you determine what the degree of a node is when it is already a difficult task to determine that you have a node at all, much less get that node's cooperation to disclose its neighbors (which, because this is a sparse network, will be few in number and no better connected than the one you've got). In addition, insurgencies tend to be highly homogeneous, with few obvious key individuals. And exceptions are often only symbolic, and so just as effective dead as they are alive.

  • Reply AutodidacticPhd September 17, 2010 at 6:02 pm

    @Ambushcrysis "…and that information would automatically get sent to a database where it would then be compared to…"

    Which would be a legal headache to set up in the first place, and the instant it was leaked to the press that someone was doing that, the site would die… practically overnight. Besides, how well connected I feel to all the people on my various friends lists changes almost daily. By the time the number crunching was done the info would be out of date.

  • Reply morthim September 17, 2010 at 10:29 pm

    @smudge6699 false, prove it. find five people between you and me.

  • Reply LudicrousTachyon September 17, 2010 at 10:33 pm

    @Ambushcrysis It sounds like something the CIA or KGB would definately abuse and I would never agree to do that. Maybe I'll do it when there are no governments that would care about picking people off.
    I'm sure your method would produce greater accuracy, but it's dangerous. While we all imagine that technology will only be used for good, time and again it has been shown to be used for evil.
    Perform an experiment with your friends. Control group, his method, yours. See how much gain yours gives

  • Reply Ambushcrysis September 17, 2010 at 10:42 pm

    @AutodidacticPhd

    people willingly providing that information for the sake of the study, and how connected you felt to the person wouldn't really matter, what would matter the most is who you know irl.

  • Reply Ambushcrysis September 17, 2010 at 10:43 pm

    @LudicrousTachyon

    I definitely agree with you. Too much power for someone to have, it would surely get abused.

  • Reply steve0281 September 18, 2010 at 2:26 am

    @urcritic I'm more worried how governments will use it.

  • Reply Galshaer September 18, 2010 at 4:35 am

    this will turn ugly.

  • Reply AutodidacticPhd September 18, 2010 at 10:15 am

    @Ambushcrysis Even if some people explicitly agree to the study, you would still have to get some form of agreement from the friends they elect. And what you mean by "know irl" is not very rigorous… there are a lot of people that I have known irl since before AOL was founded yet I never see, and on the flip side there are people I know irl that don't have a place on any of my f-lists. For a simple academic study this would be no less work and no better data than a more traditional method…

  • Reply Coreythecheese September 18, 2010 at 1:51 pm

    @Tpar1234 Brilliantly said.

  • Reply Mikael Sewerin September 18, 2010 at 4:32 pm

    I also agree, this idea – though genius, has way too much trust in the essential decency of corporate intention.

  • Reply Ambushcrysis September 18, 2010 at 5:22 pm

    @AutodidacticPhd You already got your friends permission when you request their add. As for your second comment, that's the reason I said "know irl, and list from most contact to least contact". And I strongly disagree with you on your last point.

  • Reply AutodidacticPhd September 18, 2010 at 8:09 pm

    @Ambushcrysis I'm sorry, but I don't think you fully understand the social, much less the legal ramifications of what you're suggesting. Any formal academic study of this nature would require the explicit legal consent of not only the website(s), but with each individual involved. In addition, your criteria (as stated) are nowhere near rigorous enough to ensure any more consistency or accuracy than traditional methods. And we haven't even gotten to the technical challenges.

  • Reply washaway September 18, 2010 at 8:21 pm

    @theron1n ask the people at WoW if you should exploit someones psychology for profit.

  • Reply AutodidacticPhd September 18, 2010 at 8:30 pm

    @MarxIzalias The problem you label as a "hindsight paradox" only applies if you have no previous population data, which is rarely the case. Efficacy of the application of a predictive model can be judged by comparing it to similar situations where the model was not used (or in some cases ignored or not acted upon). By building a comparative database of case studies over time the accuracy improves, and while it'll likely never reach 100%, you can still establish a better than chance efficacy.

  • Reply Barry Purcell September 19, 2010 at 8:27 am

    I think if it was actually true, it would be dangerous. However, the idea that certain "node" people have undue influence over the entire population is not new, and it's been discredited: wiki/Small_world_experiment#Critiques

  • Reply ilikebeubz September 19, 2010 at 3:39 pm

    basically he mapped out how when people get something other people follow

  • Reply Cannibalzz September 19, 2010 at 5:05 pm

    If you want to get a paper published these days, all you have to do it title it "How Social Networks [something] [something]"

  • Reply Gameboob September 19, 2010 at 7:47 pm

    With too many people on earth flu epidemics and the like are needed, if gov'ts aren't concerned with reducing population numbers.

  • Reply elchafa September 20, 2010 at 1:31 am

    This information in the wrong hands is humanity's Nemesis.

  • Reply kommaV September 20, 2010 at 3:37 am

    Google has large enough data to apply this science!

  • Reply Daniel G September 20, 2010 at 11:33 am

    What I don't understand: so if I was randomly selected for the study, they're going to ask me to nominate one of my friends? Why would I do that, and how am I supposed to decide which friend to select?

    Stupid and dangerous… look for better solutions than this.

  • Reply Fancilicious September 20, 2010 at 8:16 pm

    How exactly is this supposed to be dangerous? Google already knows everything from where you live, whom you relate yourself to, your interests etc especially if you are using google chrome.
    but what evil are they gonna use it for except maybe setting up ads according to your interests.

    Also I find it funny how everyone is so anti-1984 when they give the most intimate detail in their twitters and upload private pictures and videos for everyone to see…

  • Reply kaosgoblin September 20, 2010 at 10:10 pm

    Way to shout through your whole speech.

  • Reply omegavalerius September 21, 2010 at 1:57 pm

    @danno1111
    The idea is that more likely than not you will nominate someone who has more friends than you. You are not suppose to base your choice on any criteria. In fact the more you think about it the worse for the guys trying to study networks

    Another thing I learned in business school is that if you want to give critique you ought to have a better solution ready or at least some suggestions for improvement.

  • Reply AutodidacticPhd September 21, 2010 at 11:27 pm

    @MarxIzalias Epidemics are prior state dependent complex systems that progress through time, while lotteries are effectively instantaneous and independent events (not even systems). The methods of analysis aren't even in the same branches of maths. Your comment on how little complexity there is in a lottery should be your first clue that you have no basis for comparison.

  • Reply AutodidacticPhd September 21, 2010 at 11:34 pm

    @MarxIzalias Each event of a given system is not unique. The whole reason systems can be identified in the first place is the fact that they tend to share a large number of common variables and behaviors. The degree to which a system can be predicted is simply a matter of the accurate defining of a narrow system and whether the margin of error in the output is small enough to be useful. The universe you describe would be pure chaos, utterly incapable of supporting life, much less modern tech.

  • Reply AutodidacticPhd September 22, 2010 at 10:33 am

    @MarxIzalias "Self-refuting implication. :/"

    Hollow, self-satisfying retort. If you have a point, make it. If you just want to be smug in public, go do it elsewhere.

    "Our entire universe is a chaotic system."

    Do you have even an iota of evidence to back that up? This is pure rhetorical grandstanding.

  • Reply Bruce Wayne September 22, 2010 at 11:54 am

    THERE'S A MIC ATTACHED TO YOUR HEAD YOU SHOUTING IDIOT.

  • Reply Bruce Wayne September 22, 2010 at 11:56 am

    @omegavalerius I hate people who see everything related to business, they just appear sleazy.

  • Reply AutodidacticPhd September 23, 2010 at 5:13 am

    @MarxIzalias Look up the definition of colloquial usage. And in the future, if you want to conduct an HONEST discussion, either choose to do it entirely in public, or entirely in private. I have no time to repeat your cloakroom embarrassments for the home viewer.

  • Reply Notunder Arock September 23, 2010 at 5:39 am

    Man, TED has really been pushing the vaccine issue. Oh my they better start preparing all of us for flu season. Those vaccines companies could really use the money, I'm sure!

  • Reply TheMonsterzero September 23, 2010 at 7:56 am

    This of course is not going to be used in a good way. If you could just target central people and manipulate them, you could lead the masses in anyway you see fit. Just another way to spy to spy on the public

  • Reply jun lin September 24, 2010 at 4:40 am

    Leonard Hofstadter?

  • Reply morthim September 26, 2010 at 12:31 am

    @smudge6699
    for that to work, a person must know 6 people. i don't, and there is no reason to assume ALL other people know six people. if you can show me where the '6' people comes from then i might be able to help, but atm it just seems like a random number. also 1/72 million (or less due to shared nodes) vs 7 billion people or even the 300 million in the US… beyond that i don't think your node count is solid
    also thanks for the compliment about looking like family, in a way we all are.

  • Reply David A. Browning September 26, 2010 at 10:35 pm

    This is absolutely brilliant.

  • Reply dollaresque September 27, 2010 at 11:27 am

    wow… that is all I have to say.

  • Reply Darth Bacon September 28, 2010 at 2:55 pm

    Information can be used both good and bad.

    As long as the people use the information for good, they will hopefully be able to prevent it to be used for bad.

  • Reply AutodidacticPhd September 29, 2010 at 5:50 pm

    @MarxIzalias I'm sorry, but those statements are not contradictory. You are imposing your own opinion on them. It merely says that a system can define a set of similar events without those events being identical. If anything the statements are redundant, not contradictory. Perhaps unprecedented may be a better term than unique, seeing as you seem to think that every word that CAN have a mathematical definition must use it. I know my operators, though you seem to have a problem with English.

  • Reply Shawnruss October 4, 2010 at 8:11 am

    This seems to be a very fascist way of monitoring communication.
    Genome analyzation can also screen insurable peoples, but what is right?

  • Reply bluefootedpig October 7, 2010 at 6:49 pm

    If you saw this and the "i am my connectome"… it almost looks like the structure of society's brain. hm… i wonder what that could actually be.

  • Reply wallshuttle October 24, 2010 at 3:08 am

    Please help me. Please help me.

    Ten years of effort, we successfully developed the most advanced "Wall Shuttle" in the world eventually.
    For paint and wallpaperin. Diy new invention
    Thanks!

    Ganxing.Ke

  • Reply Ronald Schuck November 3, 2010 at 9:30 am

    This just scared the shit outta me!

  • Reply WorldCollections December 19, 2010 at 5:20 pm

    A bit persuaded…by the fake applause at the beguining.

  • Reply WorldCollections December 19, 2010 at 6:56 pm

    "The friendship paradox" hypothesis didn't hold on my facebook.

  • Reply WorldCollections December 19, 2010 at 7:05 pm

    Let's see if we could get a bunch of terrorists to nominate their friends !

  • Reply WorldCollections December 19, 2010 at 7:35 pm

    Gaytech !

  • Reply mknox242 November 19, 2012 at 5:53 pm

    I think marketers have always intuitively understand this concept. Thats why you always target the 20-30 crowd, they are the closest to the center of the human network.

  • Reply samala51 May 8, 2013 at 10:17 pm

    Data analytics are just amazing now that we have so much data at our disposal .

  • Reply Evan Stafford July 29, 2013 at 3:16 am

    I love these TED talks.

  • Reply Saloni Bhogale March 3, 2017 at 4:15 pm

    The Google Flu study has been debunked. I wonder how we can stop the promulgation of such obsolete information on the internet.

  • Reply Cultural Politics 101 September 27, 2018 at 10:28 am

    Sociology is a cultural marxist pseudoscience

  • Reply Cultural Politics 101 September 27, 2018 at 10:33 am

    Why does he think the left is big on patriotism? It's usually the right that's into nationalism over globalism or individualism.

  • Reply Atomic Bong October 30, 2018 at 8:16 pm

    How social networks create epidemics – is predictable 🙂

  • Reply Sinan Aslan October 7, 2019 at 6:42 pm

    Very sophistication.

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