Our genes influence our social networks

Chromosomes magnified - photo by BlueSunFlower

If you don’t have enough friends or aren’t the social butterfly of your class, now you can blame your genes.

Nick Christakis (Harvard Medical School) and James Fowler (UCSD political scientist) are back with more controversial findings suggesting some genetic determination in our social networks (both in forming friendships and determining where we are in social networks).  Christakis: “the beautiful and complicated pattern of human connection depends on our genes to a significant measure.”  Previous work by Christakis looked at how our social networks and who is in them shape our likelihood of obesity, happiness, and smoking, among other outcomes.

They researched 1,100 same-sex twins in the National Longitudinal Study of Adolescent Health (colloquially called “Add Health”). Add Health examined high school students in 1994-1995 and asked questions regarding economics, physical health and social involvement. Christakis and Fowler compared the social networks and patterns of identical same-sex twins against fraternal ones to separate nature (genes) from nurture (upbringing).

Their findings go far beyond what people might think about the genetic influence on personality traits (being outgoing, shy, etc.). For example, how often the subject was named as a friend and the likelihood that the subject’s friends knew one another were strongly genetically influenced, but interestingly not the number of friends that the subject listed. This suggests a genetic determinant of being popular (beyond a simple disposition toward being outgoing); further buttressing this interpretation, whether the subject was more the center of attention (central to these networks) or more of a social outcast (peripheral to these networks) was also heritable.

Christakis admits that some of the findings are puzzling, like the fact that the likelihood that my friends Bill and John know each other is attributable to my genes; what this likely means is that some people are genetically disposed to introduce their friends to each other more or to host or arrange social events where these friends would have chances to meet each other.

‘Given that social networks play important roles in determining a wide variety of things ranging from employment and wages to the spread of disease, it is important to understand why networks exhibit the patterns that they do,’  Matthew Jackson, a Stanford University economist, wrote in a commentary accompanying the study called “Do We Inherit Our Positions in Life?”.

James Fowler… said its implications go beyond the theoretical. For some time, scientists have suspected a genetic role in certain conditions, such as obesity. Now, Mr. Fowler wants to investigate whether the dynamics of social networks might affect public-health outcomes, for instance, by exposing people to certain behaviors, such as smoking.”

“Our work shows how humans, like ants, may assemble themselves into a ’super-organism’ with rules governing the assembly, rules that we carry with us deep in our genes,” says Nicholas Christakis.  Christakis et al. also believe that there may be an evolutionary explanation for their findings since one’s position in social networks had costs or benefits to the survival of one’s genes. Being central to a group likely contributed to survival during periods of food scarcity since one could learn where food supplies were, while being peripheral to groups helped genes survive in periods where deadly germs were being transmitted by social contact. Christakis: “It may be that natural selection is acting on not just things like whether or not we can resist the common cold, but also who it is that we are going to come into contact with.”  The paper notes: “There may be many reasons for genetic variation in the ability to attract or the desire to introduce friends.  More friends may mean greater social support in some settings or greater conflict in others.  Having denser social connections may improve groupsolidarity, but it might also insulate a group from beneficial influence or information from individuals outside the group.”  The authors note that more work is required to understand what specific genes are at work and what possible mediating mechanisms might be.

The authors acknowledge some controversy in studies comparing identical twin studies to fraternal twins, with critics noting that identical twins may have a stronger affiliation with  each other that causes them to be more influenced by each other than fraternal twins.  The authors note that twin studies have been validated by comparing identical twins raised apart versus together (suggesting that it is not the shared environment).  The authors further note that personality and cognitive differences between identical and fraternal twins persist even among twins mistakenly believed to be identical by their parents (indicating that parental patterns in raising these ‘identical twins’ can’t explain the outcome).  Finally, they note that that once twins reach adulthood, identical twins living apart tend to become more similar with age, which doesn’t fit with a notion of the importance of their shared environment.

The study appeared online in James Fowler, Christopher Dawes and Nicholas Christakis,  “Model of Genetic Variation in Human Social Networks” in Proceedings of the National Academy of Sciences journal (January 26, 2009).

“More specifically, the results show that genetic factors account for 46% [95% confidence interval 23%, 69%] of the variation in in-degree (how many times a person is named as a friend), but heritability of out-degree (how many friends a person names) is not significant (22%, CI 0%, 47%). In addition, node transitivity [the likelihood that two of a person's contacts are connected to each other] is significantly heritable, with 47% (CI 13%, 65%) of the variation explained by differences in genes. We also find that genetic variation contributes to variation in other network characteristics; for example, bertween-ness centrality [the fraction of paths through the networks that pass through a given node] is significantly heritable (29%, CI 5%, 39%).”

See also “Genes and the Friends You Make” (Wall Street Journal, 1/27/09 by Philip Shishkin)

See other articles by Christakis et. al on social networks.

Happiness is contagious

Nick Christakis (Harvard School of Public Health) and James Fowler (Univ. of Calif., San Diego), who previously used the Framingham Heart Study to show that having fat friends increasingly makes people obese, are back with a very interesting paper showing that happy friends make you happy — what the co-authors called ‘an emotional quiet riot’.

It is already established that happiness and having social capital (friendships) are linked, but this research demonstrates that it matters how happy your friends are and that it is the happy friends that are causing your happiness rather than vice versa. Conversely, having unhappy friends over time makes you less happy.

The research shows up in the latest issue of BMJ. “Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study.”  [The study involved 5,124 adults aged 21 to 70 who were followed between 1971 and 2003.]

They measured happiness with a 4-item construct:  “I felt hopeful about the future”; “I was happy”; “I enjoyed life” and “I felt that I was just as good as other people.”

They found that happiness is a network phenomenon, clustering in groups of people that extend out to 3 degrees of separation (the friends’ friends of one’s friends), but with greater impact on friendships that are 2 or 1 degree of separation from you.  Demonstrating the magnitude of this effect, co-author James Fowler noted, “if your friend’s friend’s friend becomes happy, that has a bigger impact on you being happy than putting an extra $5,000 in your pocket.”

They found that happiness spreads across a diverse array of social ties, from spouses to siblings to neighbors. They found no happiness effect of co-workers and found that nearby ties had a far greater influence than distant ties: for example, knowing someone who is happy, makes you 15.3% more likely to be happy, but having happy next-door neighbors makes you a full 34% more likely to be happy (much higher than having happy neighbors merely on your block). The optimal effect was for a happy friend living less than half a mile away, which boosts your chance of happiness by42%. In one of the study’s surprises, happy spouses (which one assumes live less than a half mile away!) only increased one’s chance of happiness by 8%. Part of the lower spouse effect is that happiness spreads more effectively through same sex relationships than relationships (romantic or not) between a man and a woman.  (Gays take note!)  Christakis and Fowler believe we may take emotional cues from people of our gender.

They observed that network characteristics (where you were in the network and how happy the people were around you) could independently predict which individuals would be happy years into the future.

They suggest that there may be an evolutionary basis for human emotions.  Previous work noted that emotions like laughter or smiling seemed evolutionary adapted to helping people form social bonds.  [They note: "Human laughter, for example, is believed to have evolved from the 'play face' expression seen in other primates in relaxed social situations. Such facial expressions and positive emotions enhance social relations by producing analogous pleasurable feelings in others."]

While they couldn’t prove it, they suggested 3 possible causal mechanisms:

1. happy people might share their good fortune
2. happy people might change their behavior toward others (by being nicer or less hostile)
3. happy people might exude a contagious emotion (although this would have to be over a sustained time period)

Christakis and Fowler noted that the 3-degrees of separation impact observed in happiness was the same as for smoking and obesity (which also reached out 3 degrees). They wonder whether a “3-degrees of influence” extends to behaviors like depression, anxiety, loneliness, drinking, eating, exercise and other health-related activities.

So the next time you’re unhappy realize that you may be “infecting” your friends with unhappiness as well.  Christakis’ work is suggesting that we need friends, but we also need to carefully pick friends that are happy and have healthy behaviors or we risk that their unhappiness and unhealthy behaviors will spread to us.  The New York Times notes that one of the co-authors indicated that he now thinks twice about his mood knowing that it affects others. That said, he noted: “We are not giving you the advice to start smiling at everyone you meet in New York….That would be dangerous.”

While they think that face-to-face connection is important in spreading happiness (hence the decline of these effects with distance), they did a separate study of 1,700 Facebook profiles, where they found that people smiling in their photographs had more Facebook friends and that more of those friends were smiling. While the Facebook study is just an initial foray into the online word, Christakis thinks that it shows that some of these happiness findings might extend on social networking as well.  And it would take longitudinal studies to determine whether our online activities are gradually eroding our need for face-to-face communication to spread happiness.

Note: Justin Wolfers (on the Freakonomics blog) is skeptical of this research.  As he notes:

[It's possible that it is not your friends' happiness that is causing yours, but that "if you and I are friends, we are often subject to similar influences. If a buddy of ours dies, we’ll both be less happy. Or, less dramatically, if our favorite football team wins, we’ll both be happier. But this isn’t contagious happiness — it is simply a natural outcome of the shared experiences of people in the same social circles. Unfortunately, observational data cannot distinguish the headline-grabbing conclusion — that happiness is contagious — from my more mundane alternative: friends have shared emotional influences."

Wolfers notes that a very careful article by Ethan Cohen-Cole and Jason Fletcher uses the same research design to show how it can lead to silly conclusions.  Cohen-Cole and Fletcher find in another dataset that this approach shows “height, headaches, and acne are also contagious.” As Wolfers notes, it’s more likely that “the same jackhammer causing your headache is likely causing mine.” And the height finding is obviously not causal but more likely a function of homophily (people choosing similar friends).

See Clive Thompson, “Is Happiness Catching?” (NYT Sunday Magazine, 9/13/09)

Boston Globe story available here.

New York Times story available here (which also has a nice graphic showing the clustering in this network of happy and unhappy people).

L.A. Times story available here.

See Wolfers’ Freakonomics blog post here.

Honest signals: our hidden, influential patterns of communication

(photo by shadowplay)

Interesting lunchtime talk by Alex (Sandy) Pentland about honest signals sponsored by the Program on Networked Governance program at Harvard’s Kennedy School.

Sandy’s theory is that 50,000-100,000 years ago, humans lacked language, yet still managed to communicate with each other through “honest signals” (ancient primate signaling efforts which developed biologically to communicate our intentions, our trustworthiness, our suitability as a collaborator, whether we were bluffing, etc.). When language was introduced, it didn’t over-write or eliminate these honest signals but evolved to be synergistic with these signals. While we focus much more on language, these signals are measurable (Sandy’s group developed machines to read these signals) and often equally or more effective at predicting various behaviors than language. Sandy’s research aims to shine a light on this powerful channel that we know less about.

Sandy notes that such data from electronic ID badges (sociometers) and specially-programmed smart phones, can give us a “god’s eye” view of how the people in organizations interact, and observe the “rhythms of interaction for everyone in a city”.

What are such behaviors?

Sandy’s group at the MIT Media lab focuses on 4 of them, although there are probably others (laughter, yawning, etc.).

1. INTEREST, shown by activity. An autonomic response. For example in children, this is evinced by jumping up and down or in dog’s by barking or wagging tail.
2. ATTENTION, by looking at influence. Evidence of thalmic attention. Sandy observes that people actively following in conversations break in faster than they could with normal attention spans. Shows that they are processing the conversation and discussion as it goes along and predicting the right time to break in.
3. EMPATHY, as shown by mimicry. This is evinced by mirror neurons, which are observable in infants as young as 3 hours old that can imitate a mother sticking her tongue out. People who evince higher levels of mimicry are seen as more empathic and more trustworthy. For example, they had computerized agents trying to sell an unpopular policy to students; in the cases where the computerized agent mimicked the body movements of the experimental subject with a 4 second delay, the computerized agent was 20% more successful in selling the policy to the experimental subject and the subject was unaware that he/she was being mimicked.
4. EXPERTISE, as shown by consistency. This a function of the cerebellar motor. We assume that people who can do things more smoothly are more expert because of the number of actions that need to be simultaneously coordinated.

What do these honest signals predict?
These are only some of the examples:
-Computers attentive to these honest signals (and ignoring the content) were as successful in predicting from pitches by entrepreneurs which business plans would be judged by business school students as successful.
- Effective sales pitches: listening to the first few seconds of a telephone sales pitch (without listening to the language) but listening to tone, timing, etc., the computer could predict with 80% accuracy which would be successful calls.:
-Success in speed dating: monitoring the female’s signals predicted 35% of the variation in which couples exchanged their phone numbers, and this was significantly higher than any other factor researchers could find. Interestingly, the men’s signals were not predictive, but somehow men must have been able to subconsciously pick up on the women’s signals, because in almost all cases the men didn’t ask for phone numbers where it wasn’t reciprocated by women.
- They also found that honest signals predicted depression, predicted who was likely to be successful in negotiating for a pay raise, job interviews, who was bluffing at poker, etc.
Successful individual-level traits: they found that the most successful folks with these “honest signals” were ones who were high in activity, high in influence (others were more likely to mirror their communication styles then they were likely to mirror others’) high in “variable prosody” (their pitch varied and they sounded open to ideas), and high in body language dominance (i.e., they were more likely to directly face another person and others were more likely to not face them square on).  They were often far more successful in these “honest signals” than they were aware of.

Organizational effectiveness

Sandy notes that unlike an MRI, one can hook up an entire organization to these sociometers and absorb micro-second by micro-second, and the results are highly predictive. But the challenge is that while the people who exhibit these highly successful individual traits are useful to organizations, they are usually in “connector” roles for organizations, with star-shaped patterns of communication, where ideas flow through these individuals. While this speeds up the decision-making process, it actually impairs the brainstorming process. Sandy’s group is experimenting with devices to see if making participants aware of the dynamics of a team can influence their behavior in a positive manner.  They have shown with some experiments (Japanese-American teams designing Rube-Goldberg-type projects, and distance teams) that it can change people’s behaviors in a positive manner. The challenge will be to see if the group’s behavior can be more connected at the brainstorming phase and more “star-shaped” at the decision-making stage.

Sandy noted that they have been able to extract many properties of the social networks using smart phones: from a combination of where people are (GPS), when, and communication flows (who they talk to and when). He noted some interesting experiments to observe the flow of nurses in a nursing ward, or the flow of taxis in San Francisco, or communication (e-mail and face-to-face) between departments in a German bank. They are now at the stage of trying to get whole dormitories or parts of the city of Boston using these smart phones to try to track social networks and patterns in these data. (I’ve written about digital traces before.)

How could these flows of people be used:

-Traffic: one could monitor, for example, delivery vans coursing through the road networks and by observing flows slower than typical, spot emerging traffic problems.

-Urban tribes: Sandy noted that by monitoring flows of taxis, you can distill separate patterns of interconnected places. In other words people who live in this neighborhood, work in this area, go to these restaurants, go to these nightclubs. (You are not actually monitoring individual people but patterns of association.  This is equivalent to Netflix telling you that people who like “The Firm” also like “Michael Clayton”.) Or one can even find sub-patterns in a neighborhood:e.g., locations from which people regularly are returning from nightclubs at 3 or 4 AM.

-You can then use these patterns to “find people like me”: based on your own patterns (where you work, where you live, etc.), the system could tell you where many people in your neighborhood shop, go to dinner, or hear music.

- Lending: one major bank told Sandy that credit scores are not very good (except at the high end) in predicting repayment rates on loans. Banks would love to use behavioral information (who is at nightclubs late at night, who goes to work early) to predict repayment rates.

- Health insurance: similarly one could imagine rates tied to activity levels (who was jogging or getting enough sleep or…)

- Germs: they want to use these devices to watch the spread of germs through social networks.

Privacy issues

The above examples of health insurance and lending make one understand why there are clear privacy implications. Do we want banks or health insurers knowing what we are doing (going to nightclubs) to set our rates? Will this be used to impose behavioral bases for “red lining”, where people in certain areas (like the old red lined areas) don’t get loans because of some behavior of theirs that is correlated with low repayment rates? Does it make any difference if these people can supposedly change their behavior?
-Sandy thinks we should move from company owning the personal data and sharing with no one or only sharing if an individual didn’t say it was confidential to the person owning the data and being able to decide how it gets used and whether the owner gets compensated for such use.
-There are clearly issues here about how the decision is framed? Does the individual truly understand why certain marginal information is so useful to a bank or insurer? And there may be negative externalities for all, even if you don’t choose to share your information with these companies?

Sandy’s research also raises questions about what happens when you start incentivizing people in companies based on these behaviors, or you start teaching people about these hidden “honest signals”. Do people start learning how to display these honest signals and dupe people who are not as aware of this (e.g., mimicking others to increase sales or do better in negotiations). If so, do people start focusing on these behaviors (like mimicry) and consciously teach themselves not to be swayed by this? Do companies find that people who pretend to be connectors (to get a pay raise) are actually less valuable to companies than the people who do it naturally (and are unaware they are doing this)?

See previews of Sandy’s book Honest Signals here.

Buy Alex (Sandy) Pentland’s Honest Signals here.

See interesting related story in NYT, “You’re Leaving a Digital Trail. Should You Care?” (John Markoff, 11/30/08), mentioning Alex Pentland’s work among others and discussing the SF taxi example.

UPDATED: Crowdsourcing to replace social networks?

Mark Pesce writes in “This That and the Other Thing” the following:

“The easy answer is the obvious one: crowdsourcing (see also description later in post). The action of a few million hyperconnected individuals resulted in a massive and massively influential work: Wikipedia. But the examples only begin there. They range much further afield.

“Uni [University] students have been sharing their unvarnished assessments of their instructors and lecturers. Ratemyprofessors.com has become the bête noire of the academy, because researchers who can’t teach find they have no one signing up for their courses, while the best lecturers, with the highest ratings, suddenly find themselves swarmed with offers for better teaching positions at more prestigious universities. A simply and easily implemented system of crowdsourced reviews has carefully undone all of the work of the tenure boards of the academy.

“It won’t be long until everything else follows. Restaurant reviews – that’s done. What about reviews of doctors? Lawyers? Indian chiefs? Politicans? ISPs? (Oh, wait, we have that with Whirlpool.) Anything you can think of. Anything you might need. All of it will have been so extensively reviewed by such a large mob that you will know nearly everything that can be known before you sign on that dotted line.

“All of this means that every time we gather together in our hyperconnected mobs to crowdsource some particular task, we become better informed, we become more powerful. Which means it becomes more likely that the hyperconnected mob will come together again around some other task suited to crowdsourcing, and will become even more powerful. That system of positive feedbacks – which we are already quite in the midst of – is fashioning a new polity, a rewritten social contract, which is making the institutions of the 19th and 20th centuries – that is, the industrial era – seem as antiquated and quaint as the feudal systems which they replaced.”

He suggests that these e-connections and contributions can in effect tell us which restaurant can be trusted to eat at, which professor we can entrust to teach us a class.  In principle, one could use this to also pass on social reputation with pictures and names for community residents who had behaved in an untrustworthy manner so others could avoid them.  On its face it sounds like a persuasive argument and part of a strand that suggests that the new technology can always out-do what we used to do.  Assuming the software is effective at eliminating shills (as eBay or Amazon had to contend with — writers or sellers getting fake users or affiliated users from giving them great reviews), these kind of crowdsourcing techniques can be helpful.  Yelp’s recommendations about restaurants are often good; and Amazon’s recommendations are instructive.

What can’t these invisible, helping e-networks do?  1) get at the truth with contested theories of what happened; 2) tell you whether you should value A’s comments more than B’s (although in principle the software could rate the comments by friends in common or their reputation); 3) actually be useful for things beyond spreading information (trust, reciprocity, social support, etc.).

Pesce goes on to point out that the technology does have limits.  Technology brings us together in anarcho-syndicalism and offers the potential for community.  But what limits its effectiveness is that we have a collision between the e-crowd and community and community requires us to work together.  We want to copy and mimic what others have done, but that requires each of us to act for the good of others.

“But [our] laziness, it’s built into our culture. Socially, we have two states of being: community and crowd. A community can collaborate to bring a new mobile carrier into being. A crowd can only gripe about their carrier. And now, as the strict lines between community and crowd get increasingly confused because of the upswing in hyperconnectivity, we behave like crowds when we really ought to be organizing like a community.

And this…is..the message I really want to leave you with. You … are the masters of the world. Not your bosses, not your shareholders, not your users. You. You folks, right here and right now. The keys to the kingdom of hyperconnectivity have been given to you. You can contour, shape and control that chaotic meeting point between community and crowd. That is what you do every time you craft an interface, or write a script. Your work helps people self-organize. Your work can engage us at our laziest, and turn us into happy worker bees. It can be done. Wikipedia has shown the way.

And now, as everything hierarchical and well-ordered dissolves into the grey goo which is the other thing, you have to ask yourself, “Who does this serve?”…I want you to remember that each of you holds the keys to the kingdom. Our community is yours to shape as you will. Everything that you do is translated into how we operate as a culture, as a society, as a civilization. It can be a coming together, or it can be a breaking apart. And it’s up to you.”

What Pesce doesn’t discuss is “social capital.”  This seems to be missing from his remarks.  Some of us may serve others in real space or electronically through the goodnesss of our hearts.  We’re do-gooders or e-do.gooders.  But others of us need to understand that these social ties hold us accountable to the group.  They make us more likely to do things for others because we are hardwired to provide more for people inside our circles than outside our circles.  That’s why we give more to our family than to strangers and help friends more than we do a tribe half-way around the world.  Social ties redefine our sense of ‘we’.

It’s hard to believe that exhortations to do good on the Internet, as important as they are, will achieve the optimal amount of communal action.  That is, after all, why commons are overgrazed and oceans are overfished.  Because too many in society realize that there is more to be had from overgrazing and overfishing now rather than letting someone else do it.

Social capital can also help police social norms (of working for others, of contributing, of not taking more than one’s share).  Experimental evidence shows that fairness also seems hardwired into our brains.  We are willing to punish others in experimental Ultimatum or Dictator Games from behaving in a selfish manner, even when it means that we the punisher gets less.

For more on Crowdsourcing, Jeff Howe (from Wired) has an interesting new book, Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business (2008).

Definition: A company outsourcing a job traditionally served by employees and fills it through an open call to large undefined group of people, generally using on the internet.  People best qualified to do the job are not always the person that one would first think of to assign a job in a corporation.

CrowdSourcing builds upon The Wisdom of Crowds; in it, Howe identifies 4 ways in which groups can produce better results than individuals: collective intelligence, crowd creation, crowd voting, and crowd funding.

From BusinessWeek’s review of the book: “In the first [category], collective intelligence, companies including Dell and gold-mining group Goldcorp ask people inside and outside the company to help solve problems and suggest new products, such as Dell’s Linux-based computers. The second model, crowd creation, is used by businesses such as Current TV and Frito-Lay to create news segments and video ads. People vote for their favorite T-shirt design at apparel maker Threadless’ Web site, thereby illustrating crowd voting. Startups SellaBand and Kiva use the last model, crowdfunding, to underwrite new music labels and fund microloans to individuals.

“Howe’s best example is iStockphoto, a startup that is undermining the established stock-photo business. The community began in 2000 as a vehicle for hobbyists who wanted to trade their pics. Two years later, iStock began selling photos for 25 cents each to cover bandwidth costs. Clients flocked in, and in 2006, Getty Images bought the enterprise. Now, with 60,000 part-time photographers and illustrators on board, 3.5 million images in the bank, and 2 million customers, iStock is the world’s third-largest dealer of images.

“Howe sweeps away certain misapprehensions about such activity. While it’s true that most people who are involved don’t get paid, they still need incentives. At iStockphoto, that comes in the form of workshops in which people meet and share expertise. And Howe warns that not all crowds are created equal. For example, he suggests that sports teams would do better to use fantasy-league enthusiasts rather than scientists to handicap up-and-coming athletes. Perhaps the hardest lesson for businesses is the importance of including people with whom you don’t ordinarily work. Organizations reinforce similar approaches and inside-the-box thinking. When you’re looking for something truly different, the crowd can lead you down a less traveled path.”

While Howe praises this rise of the ‘virtual crowd’ — you used to have to actually assemble a crowd to benefit and now gee-whiz you can do it on the internet — I wonder whether despite benefits to corporations or individuals (like cheaper pictures on iStockPhoto or better predictions of what ads will work), we’ve lost the social capital inherent in actual crowds or the social capital built from these old-line processes.

If we are migrating to more CrowdSourcing we ought at least pursue what we do (at a minimum via the Internet) to actually bring this virtual crowd together (making creating e-events, maybe creating communities of interest as was the genesis of iStockPhoto, maybe if the virtual crowd is large enough, breaking it down by zip code and encouraging and facilitating pieces of the crowd getting together in real space).  What’s good for the goose is not always so for the gander, and CrowdSourcing is likely to lead to cheaper outcomes (for example photos) and often better, more democratic decisions, it portends to exacerbate the real losses we’ve seen in our true communities over the last generation.

10/7/09 update: Facebook, through Facebook Connect, now uses crowd-sourcing for foreign language translation, getting users to vote on which user-supplied translations are best for various phrases.  More here:

Clever Obama iPhone application to use social networks

The Obama campaign has released an application for the iPhone that cleverly sorts your address book, prioritizing which friends you should call to convince them to vote Obama.  (“Call Friends” sorts your friends by how close the race is in that state. So you can call your Ohio friends or Missouri friends and not bother with your California or NY friends.)  It’s a smart marrying of the fact that friends are much more likely to convince friends politically, coupled with the technology that helps you to easily see where your social networks may make the most political difference given battleground states and the electoral map.  The ‘Get Involved’ Button uses GPS to help you find the closest Obama campaign headquarters.

Another interesting part of the application is that it shows how many calls you have made using this application and how many have been made nation-wide, enabling one to feel a growing sense of momentum and part of a larger national cause.  (The software doesn’t transmit who you called, but records the number of calls made with the application so they can centrally keep track.)

Download the Obama for America iPhone application here.

Here is the blog post of its developer Raven Zachary.

See earlier blog posts of mine about use of technology in the Obama 2008 campaign.  See also this one and this one.

Make that at least 7 Degrees of Separation

An interesting study by two Microsoft researchers (Eric Horvitz and Jure Leskovec) crunched the records of 30 billion electronic conversations among 180 million people from around the world, and found that any two people on average are separated by…..drumroll please….

6.6 degrees of separation. That means it would take 7 people or less to connect 78% of the pairs in their sample.

What the study doesn’t seem to address is that *6 degrees of separation* was meant to be a maximum (i.e., all people are connected by 6 or fewer links) not an average and the fact that the 180 million people using Microsoft’s IM Messenger are likely more connected than the most socially isolated individuals in the world. So on both fronts, I’d expect that the maximum chain length is longer than the 7 degrees of separation they found. Balancing this on the other side, it is very possible that if more individuals were included beyond the 180 million they included that one would find shorter social paths that went outside this group of 180 million individuals. The researchers note that just in their database, some of the 180 million individuals took as many as 29 links to connect.

The researchers considered individuals to be acquaintances if they had sent one another a text message.

The database they used was anonymized but covered all of the Microsoft Messenger instant-messaging network in June 2006, or roughly half the world’s instant-messaging traffic at that time.

See an earlier post on the 6 degrees of separation issue here.

Researchers claimed that this research could aid political organizations, charity efforts, natural disaster relief and missing-person searches.”They could create large meshes of people who could be mobilized with the touch of a return key,” Horvitz said.

View the paper, “Planetary-Scale Views on a Large Instant-Messaging Network” here.

And see Bill Sherman’s interesting comment below.

Quit your habit in groups and be more popular, say scientists

Nick Christakis and James Fowler made headlines recently for their study on obesity contagion (See Can your friends affect your weight?).

Now they’re back with analogous research that shows that you are far more likely to be able to quit smoking if you do it in groups (where those around you are also quitting). It’s scientifically-proven, but something that practitioners have known for a while: why do you think Jenny Craig has dieters work in groups, or why all the self-help groups (Alcoholics Anonymous and others of that ilk) use group norms to reinforce changes in behavior.

Study co-author Fowler notes that in tracking individuals and social groups (through the Framingham Heart Study) over 30-years, the average size of each cluster of smokers was of similar size, but Fowler notes: “It’s just that there are fewer and fewer of these clusters as time goes on.”

The social contagion of quitting smoking can extend to people that the quitter didn’t know. For example if Anne quits smoking, and Anne is friends with Barb and Barb is friends with Clarissa, Clarissa’s chance of quitting increases by 30%, even if Anne and Clarissa don’t know each other.

Christakis notes that smokers have moved more to the periphery of social networks, where they often were more at the center of these social networks several decades ago. While this doesn’t say that teen non-smokers will necessarily be more popular, it does suggest over their lifespan that non-smoking is more likely to be associated with popularity than smoking.

The obesity study appears in the May 22 issue of the New England Journal of Medicine.

See also, Clive Thompson, “Is Happiness Catching?” (NYT Sunday Magazine, 9/13/09)

Social networking becoming more invisible but more ubiquitous?

The Economist notes that while social networking efforts haven’t found profitable financial models, there is evidence that they are migrating to more of a common model that is less proprietary and more in the background, like air.

“Historically, online media tend to start this way. The early services, such as CompuServe, Prodigy or AOL, began as ‘walled gardens’ before they opened up to become websites. The early e-mail services could send messages only within their own walls (rather as Facebook’s messaging does today). Instant-messaging, too, started closed, but is gradually opening up. In social networking, this evolution is just beginning. Parts of the industry are collaborating in a ‘data portability workgroup’ to let people move their friend lists and other information around the web. Others are pushing OpenID, a plan to create a single, federated sign-on system that people can use across many sites.

“The opening of social networks may now accelerate thanks to that older next big thing, web-mail. As a technology, mail has come to seem rather old-fashioned. But Google, Yahoo!, Microsoft and other firms are now discovering that they may already have the ideal infrastructure for social networking in the form of the address books, in-boxes and calendars of their users. ‘E-mail in the wider sense is the most important social network,’ says David Ascher, who manages Thunderbird, a cutting-edge open-source e-mail application, for the Mozilla Foundation, which also oversees the popular Firefox web browser.

“That is because the extended in-box contains invaluable and dynamically updated information about human connections. On Facebook, a social graph notoriously deteriorates after the initial thrill of finding old friends from school wears off. By contrast, an e-mail account has access to the entire address book and can infer information from the frequency and intensity of contact as it occurs. Joe gets e-mails from Jack and Jane, but opens only Jane’s; Joe has Jane in his calendar tomorrow, and is instant-messaging with her right now; Joe tagged Jack ‘work only’; in his address book. Perhaps Joe’s party photos should be visible to Jane, but not Jack.

“This kind of social intelligence can be applied across many services on the open web. Better yet, if there is no pressure to make a business out of it, it can remain intimate and discreet. Facebook has an economic incentive to publish ever more data about its users, says Mr Ascher, whereas Thunderbird, which is an open-source project, can let users minimize what they share. Social networking may end up being everywhere, and yet nowhere.”

View full Economist story here.

Social networking: a social “eden” or dystopian tool?

Freakonomics convenes an e-forum of various researchers of the social effects of social networking sites. As we’ve noted on this site, research on the social impact of these sites is still in its infancy, and we’ve blogged before on what’s interesting and the limitations of Nicole Ellison’s MSU study. On balance, most of the researchers see that there will be both desirable and undesirable byproducts of these social networks.

I agreed with some of the interesting comments of Will Reader (we blogged about his study earlier): “Some doom-mongers have suggested that social networking technologies will eventually lead to a society in which we no longer engage in face-to-face contact with people. I don’t see it. Face-to-face contact is, I believe, very important for the formation of intimate relationships (and most of us crave those).” But since close friendships require large investments of our time and emotion, we want to make sure that others are worth this investment, and non-verbal cues obtained from face-to-face interactions are one of the best ways to gauge this.

Reader notes that “talk is cheap” on social networks. “Anyone can post “u r cool” on someone’s “wall,” or “poke” them on Facebook, but genuine smiles and laughs are a much more reliable indicators of someone’s suitability as a faithful friend….To return to the notion of social capital, we know that people are increasingly “meeting” people on social network sites before they meet them face to face. As a result of this, when many students begin university, they find themselves with a group of ready-made acquaintances. Given people’s preferences for people who are like them, it could be that friendship networks become increasingly homogeneous. Is this a bad thing? It might be if, by choosing potential friends via their Facebook profiles, it means that folk cut themselves off from serendipitous encounters with those who are superficially different from them, ethnically, socio-economically, and even in terms of musical taste.”

Reader believes that social networking will change our society but one’s own preferences will dictate whether it is a utopian or dystopian future.

We’ve written earlier about how the Internet is an especially efficient way to maintain social ties that were made face-to-face, but Reader is undoubtedly true, that for geographically based networks (like Facebook, generally centered on college campuses), Facebook activity prior to coming to college campuses may accelerate the process of making new friends and may also exacerbate our tendency to form bonding friendships at the expense of bridging.

And Judith Donath makes the point that we have made earlier that social networking sites often “cheapen” the currency of friendships.

See whole forum here. [Freakonomics asked Martin Baily, Danah Boyd, Steve Chazin, Judith Donath, Nicole Ellison, and William Reader:" Has social networking technology (blog-friendly phones, Facebook, Twitter, etc.) made us better or worse off as a society, either from an economic, psychological, or sociological perspective?"]

Hive Intelligence

I heard an interesting talk by Rob Goldstone from Indiana University on 3/3/08, courtesy of the Program on Networked Governance at Harvard’s Kennedy School.

Rob studies how individuals each performing their own actions exhibit group properties, what could be called “hive intelligence.” In other words, without a dictator telling each individual how to act, there may still be observable and interesting patterns that emerge. (Each individual contributes to the overall pattern, even though no one individual’s behavior dictates it and the result may be unforeseen by any individual.) This “hive intelligence” has been shown to be the case for example in which there are observable patterns for where Saguaros send out branches (close to our heart), the distribution of sunflower seeds, the stripes on zebras or other natural properties, but can also be found in human behavior (imitation, traffic patterns, pedestrian traffic, etc.). He studies this largely through experiments (in laboratories, over the Internet, in virtual worlds like Second Life where he notes it is very easy to recruit volunteers and pay them for their experimental performance in Linden dollars).

He discussed, in detail, two experimental areas: 1) foraging behavior; and 2) imitation/innovation.

In the foraging experiments, human subjects are trying to forage as much virtual food as possible. Food packets are dropped on screens every 4/N seconds, where N is the number of human subjects. The food is generally dispersed in two circular areas of the same size and the distribution of the food between the two is experimentally altered to be either 50/50, 65/35 or 80/20 across the two areas. When a subject moves to a square containing a food packet, he/she acquires the food and the food packet disappears. Participants play in various conditions where either the other food packets are visible and/or the other players’ positions are visible. Rob finds some inefficiencies in foraging, especially in the invisible condition (where you can’t see where other players are and where the food is). Specifically they find extra scatter (respondents are not as closely honed in on where the food is), there is undermatching (that is that more people hang out at the less populated food site and they would harvest more food at the other site that has more food), and they observe population cycles. The population cycles refer to the fact that people’s desire to avoid crowds, actually leads to them to greater crowding; in the visible condition, people are tempted to move to another site, but they observe others moving to that site and talk themselves out of moving; in the invisible condition, without this feedback loop of others’ behavior, they are all more likely to move and hence recreate the crowding they sought to avoid. On the undermatching front, it looks like evolution must have favored those who avoided more populated food sites (perhaps because food was more scarcely distributed and hence there were lower returns from all being at the same site), and thus we are conditioned to favor avoiding being at the more popular location, even when it would lead to greater food harvesting. (Obviously while the experiment was narrowly about food harvesting, it could equally well apply to things like traffic patterns, etc.) They also observed a certain level of inertia: that people, all things being relatively equal, were more likely to stay where they were than move to another square on the food foraging game. Rob also notes that there are greater inequalities of outcomes in the invisible condition, especially with an 80/20 distribution of food, since people who happen upon the food stashes are more likely to acquire a lot of while others are still exploring to find the food sources. And finally, they observed the importance of knowledge: for example, when the location of other agents was visible but not the food packets, people observed “buzzarding” behavior, assuming that the presence of other agents was an indication that there was prevalent food there, and hence there was greater herding behavior. (This is reminiscent of Communist Russia where people would instantly get in line when they saw a long line outside of a store, assuming that the store must be offering some really good food.) A paper on this available here and the simulation available here.

They also did some experimentation with innovation and imitation. Groups need both of these to prosper: too much innovation and you don’t have effective dissemination of good new ideas and too much imitation and you underinvest in exploring new ideas and better solutions. He tested how much of each occur in 4 different type of networks:

1) Lattice: a world in which everyone is connected to their immediate neighbors and maybe one near neighbor (looks like a ring network with people also connected to near and not only immediate neighbors)
2) Fully connected (everyone connected to everyone else)

3) Random (people have some number of links as lattice but it is random to whom they are connected)

4) Small world: basically a lattice network (where you know your neighbors but with a few random links thrown in that shorten the distance dramatically between any two actors in the network.

They asked participants to maximize points and there was a hidden function (graph) that either had a single peak or 3 peaks. Participants would play 15 rounds and at the end of each round learn their score and the guesses of the others in the network to whom they were connected and what scores they received.

Results: for a single peaked problem, the fully connected network does the best earliest, since it disseminated information quickly, but the other networks catch up over 15 rounds. For a 3-peaked problem, the small world does better. The fully connected network leads to premature bandwagoning where they settle on a local maximum but not the global peak. The small world combines some dissemination with enough local niches to permit continued experimentation and innovation. Participants were more likely to explore early on than later and less likely to imitate (strangely enough) in late rounds. For really hard problems, like a needle in a haystack type of function, the lattice network actually does best because it fosters the most innovation. Rob acknowledged that in these experiments, the fully connected model, since you learn the guesses of all the other participants may lead to information overload. The results are consistent with some earlier research by my colleague David Lazer. One questioner noted that a fully connected network may be bad for the Delphi decision making process, since if you learn everyone’s views too early it may inhibit greater innovation in the exploration of options/solutions. Rob noted that in order to effectively configure a work team you both need to know something about the nature of the problem being solved (is it closer to the one-peak solution, the 3-peak solution or the needle in a haystack) and something about the disposition of team members (are they naturally people whose inclination is to imitate or innovate). David Lazer raised an interesting point that in networks where certain people in the network play a disproportionately critical role in sharing information (bridgers in a small world network or hubs in a scale-free network), there may be greater variation in the results depending on how effective that bridger or hub is in sharing key insights; if that person basically does what he/she wants to and ignores others’ learning, it effectively dismantles a key portion of information sharing and potential imitation. Paper on innovation/imitation available here.

Rob’s perception Lab, where one can participate in experiments, can be found here. Rob noted that when there are not enough live volunteers, the volunteers play against ‘bots, based on models of human behavior and Rob’s goal is an adapted form of the Turing Test, where volunteers don’t know if they are playing against humans or ‘bots.

After the presentation Rob explained that he is also doing some experimental work on the commons. Together with Elinor Ostrom, he has an experiment on foraging where if food is left, other food can grow in adjacent cells. Obviously this depends on the food not being immediately harvested. Their game enables the participants to develop rules for working together and agreeing to limit harvesting rights; under such regimes groups typically agree to enable property rights and do better long-term under such a system. But Rob said he was discouraged and surprised that even experimental participants who participated in such a commons experiment will immediately ignore their learning when they then play the foraging game (in a variant where new food does not continually reappear) and act on their own personal short-term self-interest, with the result that the food in the world is quickly overharvested and nothing else can grow in the later rounds. The participants bemoan the outcome but feel powerless and believe that others will also overharvest so they want to get in while the going is good; a classic prisoner’s dilemma outcome.

Also briefly discussed the concept of stigmergy (basically where individuals in an ecosystem alter the ecosystem for others, and hence alter their behaviors). One can think of people making a shortcut across the grass which as the grass is worn down induces others to follow, or think about the pheromones that an ant lays down from its trail that induces others to follow its path. Rob said one can visualize a jungle where the first intrepid explorer machetes a path through (at great effort) and this induces more people to follow which leads to a trail, which leads others to put down a pebble base which leads to a road which leads to a 4-lane highway. In this typology, for better or worse, we can visualize human progress.Rob Goldstone noted that viral popularity (which I’ve written about earlier) has some of those same properties. In other words, by buying books on Amazon or watching videos on YouTube, we lay down pheromones that say that this is good, or that people who like X also like Y, and the programs on Amazon (recommendations) or You Tube viral videos watch these digital traces and use this to induce others to follow our paths. [Incidentally, I gather such shortcut paths are known as "desire paths"; sample images here.]