Category Archives: social dynamics

The science of friendship

Flickr/JimBoudThere is an interesting article by Robin Dunbar in The New Scientist: Dunbar’s Number was named after Robin, from his theorizing that humans only had the brain capacity to manage roughly 150 relationships, although depending on gender, social skills and personality, this number could vary from 100-250.  Dunbar observes that communication often breaks down when one exceeds 150 individuals (as evidenced in the Crimean War by the Charge of the Light Brigade) and the modern military and businesses only exceed these limits through strict hierarchies.

Dunbar theorizes that language, laughter and communal music-making evolved as a way to stay connected to a larger group of individuals than possible through physical acts like grooming. Dunbar: “[N]ot only can we speak to many people at the same time, we can also exchange information about the state of our networks in a way that other primates cannot. Gossip, I have argued, is a very human form of grooming.”  Christakis and Fowler (in the excellent book Connected) note that “…language is a less yucky and more efficient way to get to know our peers since we can talk to several friends at once but only groom them one at a time.  In fact, in a conversation with a small group, we can assess the behavior, health, aggressiveness, and altruism of several individuals simultaneously.  Plus, we can talk to someone else while engaged in another activity, like foraging for food in a refrigerator.”  Christakis and Fowler note how radical the idea is that language evolved not primarily as a way to exchange information but to maintain group cohesion.   “Dunbar estimates that language would have to be 2.8 times more efficient than grooming in order to sustain the [average] group size seen in humans” (one speaker per 2.8 listeners).

While language may have originally evolved, as per Dunbar, to maintain a slightly larger group size, once developed it was in principle possible to use language to maintain social relations on a tribal or national level.

A few other excerpts from Dunbar’s article:

Group living needn’t tax your intelligence too much. In a loose herd, cues such as body size or aggressiveness may be enough to judge whether you should challenge or steer clear of another individual. In bonded networks, however, you need to know each member’s personal characteristics and those of the friends and relations that might come to their aid. Keeping track of the ever-changing web of social relationships requires considerable mental computing power.

As a reflection of this, there is a correlation between the size of a species’ brain– in particular its neocortex– and the typical size of its social groups. In other words, brain size seems to place a limit on the number of relationships an individual can have. This link between group size and brain size is found in primates and perhaps a handful of other mammals that form bonded societies such as dolphins, dogs, horses and elephants. In all other mammals and birds, unusually large brains are found only in species that live in pair-bonded (monogamous) social groups.

As group size increases so too does the number of relationships that need servicing. Social effort is not spread evenly. Individuals put most effort into their closest relationships to ensure that these friends will help out when they need them. At the same time they maintain the coherence of the group. As a result, social networks resemble a nested hierarchy with two or three best friends linked into larger groupings of more casual friends, and weaker relationships bonding the entire group. This hierarchy typically has a scaling ratio of three– each layer of decreasing intimacy is three times larger than the one before it….

HUMAN SOCIAL NETWORKS

Our social networks can have dramatic effects on our lives. Your chances of becoming obese, giving up smoking, being happy or depressed, or getting divorced are all influenced by how many of your close friends do these things. A good social network could even help you live longer since laughing with friends triggers the release of endorphins, which seem to “tune” the immune system, making you more resilient to disease. So what factors influence the form and function that our social networks take.

In traditional societies, everyone in the community is related to everyone else, either as biological relatives or in-laws. In post-industrial societies this is no longer true– we live among strangers, some of whom become friends. As a result, our social circles really consist of two separate networks– family and friends– with roughly half drawn from each group.

Because the pull of kinship is so strong, we give priority to family, choosing to include them in our networks above unrelated individuals. Indeed, people who come from large extended families actually have fewer friends. One reason we favour kin is that they are much more likely to come to our aid when we need help than unrelated individuals, even if these are very good friends.

Family and friend relationships differ in other important ways, too. One is that friendships are very prone to decay if untended. Failure to see a friend for six months or so leaves us feeling less emotionally attached to them, causing them to drop down through the layers of our network hierarchy. Family relationships, by contrast, are incredibly resilient to neglect. As a result, the family half of our network remains constant throughout most of our lives whereas the friendship component undergoes considerable change over time, with up to 20 per cent turnover every few years.

More than 60 per cent of our social time is devoted to our five closest friends, with decreasing amounts given over to those in the layers beyond, until at the edge of the 150 layer are people we perhaps see once a year or at weddings and funerals. Nevertheless, the outer reaches of our social networks have a positive role to play. The sociologist Mark Granovetter at Stanford University in California has argued that these weak links in our social networks are especially useful in the modern world. It is through this widespread network of contacts that we find out about job vacancies and other economic or social opportunities. More importantly, perhaps, 70 per cent of us meet our romantic partners through these contacts.

Read “Getting Connected” by Robin Dunbar (New Scientist, 4/3/12)

The Smoking Gun: Friends influence your smoking and quitting

New research by Professor Edward Glaeser (director of Harvard Kennedy School’s Taubman Center for State and Local Government) and David Cutler (economics professor at Harvard) shows that people are more likely to smoke when surrounded by smokers.  But positively, it shows a multiplier effect of smoking bans: those who quit smoking in turn influence their friends and spouses to stop smoking.

More specifically, “individuals whose spouse faced a workplace smoking ban were less likely to smoke themselves. The estimates suggest a 40 percent reduction in the probability of being an individual smoking if a spouse quits,” the authors write.   Cutler and Glaeser think that policy makers should consider these multiplier benefits when they are considering enacting smoking bans.

For more detail you can read their Kennedy School Working Paper, “Social Interactions and Smoking.”

I posted earlier on research on the sociological dynamics of obesity, depression, and some prior evidence on smoking (read further down in this link).

Super Crunchers: the social effect

Ian Ayres interesting new read Super Crunchers predicts a world in which humans’ intuition is increasingly replaced by machines mining enormous databases of information for connections that humans cannot perceive (who buys what products or what products to recommend based on past purchases, what illnesses could explain what symptoms, who would make good employees, etc.).

The book leaves me wondering what the human effects will be of this. Ayres predicts that humans will provide input to this machines (e.g., providing the three potential product names that will be tested by human behavior, or programming the machines or figuring out how to store these data).

I think there will be other social effects upstream and downstream:

1) many workers will feel that their work will be threatened and refuse to code information that is in their head or miscode information to make the computers’ performance look worse and try to preserve their jobs. [The WSJ had a story maybe 8 years ago set in the deep south about workers not trusting management and fighting their efforts to code their knowledge, for exactly these same reasons.]

2) will recommender systems erode one of the benefits of friendships and psychologically make us feel that friends are less valuable? (i.e., we’ll rely more on the wisdom of crowds rather than the idiosyncracies of specific friend that may have less predictive accuracy in telling us things we might like)

3) America is almost unique for most Americans’ belief in God and participation in religion. Part of what underlies this involvement is a search for meaning and a belief in what is unknowable, must be taken on faith or experienced. In the same way as science seems to conflict with faith, the cold calculations and regressions of *Super Crunching* are at odds with serendipity, epiphany, and a mysterious unfolding. How will the tensions of *Super Crunching* and faith play themselves out: with lesser degree of faith; with limits on our adoption of super crunching; or with us living what appear to be paradoxical lives that celebrate faith and super crunching.

4) how will supercrunching help when faced with problems for the first time or where you need new paradigms?

5) will supercrunching lead to a race to the bottom: will humans following computer scripts preempt important interactions or flexibility?

I’d welcome your comments about what you predict Super Crunchers’ social impact will be.

Virtual simulations of social dynamics

Various pieces raise the question of simulating social dynamics online. Researchers already had agent-based modeling, but the problem with agent-based modeling is that the models are only as good as the assumptions. Why not better to take a site that has humans interacting and let THAT be the laboratory?

Recently, an accidental and virtual spread of “corrupted blood” disease through the World of Warcraft MMOG (massively multiplayer online game) has intrigued researchers. Tufts University School of Medicine are studying the spread of that ‘plague’ to better understand the spread of pathogens among humans. Obviously, this will be useful only to the extent that the interaction of players on WoW mimics the interaction of humans in real life, and to the extent that the way that the disease spreads on WoW mimics the way it is caught in the real world. But researcher Professor Nina Fefferman, from Tufts University School of Medicine, noted: “Human behaviour has a big impact on disease spread. And virtual worlds offer an excellent platform for studying human behaviour…..The players seemed to really feel they were at risk and took the threat of infection seriously, even though it was only a game…” The virtual setting helps epidemiologists who normally have to rely only on observational and retrospective studies; in virtual worlds they can watch a virus spread in real time without anyone being hurt. And in principle, with the permission of software developers, or potentially on an open source type platform like Second Life, researchers could actually unleash a virus and watch it spread, although they would have to ensure that the software permitted others to ‘catch’ this virus.

Second Life may not be good at some simulations like infections because it may tell you who converses with whom but can’t actually show the transmission of illnesses since it is only a virtual encounter.

Already a Second Life site is being used as a lab to help people gain empathy about schizophrenia. [See also an Educause Connect blog post about the potential of SL for empathy.]

One wonders whether ‘bots operating in Second Life could also be used to track the social effects of things like prejudice for example, or friendship formation. Bots could be programmed to respond in different ways to different people and monitor the effect, or respond in the same way with others but be ‘housed’ in different avatars (white, black, Asian or Hispanic) and gauge the impact on friendship networks or prejudice. In universities, one normally has to get human subject review for any experiments that could effect people. Would the same apply to randomized studies of social interactions on SL or another MMOG site? And one might be able to randomize who one interacts with (at least from people in an MMOG) that wouldn’t be as easy in real life.

As always the concern would be whether people behave the same in Second Life as F2F (face-to-face), but it may be a more realistic venue than agent-based modeling.

BBC news story or Lancet Infectious Diseases story on the spread of the ‘corrupted blood’ on WoW and how it interested researchers.

[thanks to David Pescowitz at Boing Boing for the heads up about this WoW story]

Related: NY Time’s Tierney’s labs also had a post on simulating universe on computers. How powerful would the computer have to be? How accurate would the simulation be?]

(See also later related Social Capital Blog story about “Hive Intelligence“)