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Three degrees of influence

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Social networks theory

Three degrees of influence is a theory in the realm of social networks,[1] proposed by Nicholas A. Christakis and James H. Fowler in 2007. This argument is basically that peer effects need not stop at one degree of separation. Rather, across a broad set of empirical settings, using both observational and experimental methods, it has been observed that the effect seems, in many cases, to no longer be meaningful at a social horizon of three degrees of separation.

The theory has since been explored by scientists in numerous disciplines using diverse statistical, mathematical, psychological, sociological, and biological approaches. Numerous large-scale in-person and online experiments have documented this phenomenon in the intervening years.

Beginning in the early 2000's, Christakis and Fowler explored the impact of social connections on behavior, describing how social influence and social contagion do not end with the people to whom a person is directly connected. People influence their friends, who in turn influence their friends, and so on. Hence, a person's beliefs and actions can influence people they have never met, to whom they are only indirectly tied.

Using both observational and experimental methods, Christakis and Fowler examined diverse phenomena, such as obesity, happiness, cooperation, voting, and other behaviors and beliefs. Investigations by other groups subsequently explored many other phenomena in this way (such as crime, social learning, etc.).

In short, Christakis and Fowler posited that diverse phenomena "ripple through our network, having an impact on our friends (one degree), our friends' friends (two degrees), and even our friends' friends' friends (three degrees). Our influence gradually dissipates and ceases to have a noticeable effect on people beyond the social frontier that lies at three degrees of separation."[2] They posited a number of reasons for this decay, and they offered informational, psychological, and biological rationales.

Rationale<br>[edit]

Influence might dissipate after roughly three degrees (to and from friends' friends' friends) for at least three reasons, Christakis and Fowler proposed:[2]

Intrinsic decay—corruption of information, or a kind of "social friction" (like the game telephone).

Network instability—social ties become unstable (and are not constant across time) at a horizon of more than three degrees of separation.

Evolutionary purpose—we evolved in small groups where everyone was connected by three degrees or fewer and so we might not have the capacity to detect or respond to weak "signals" emanating from further away geodesically (an idea receiving subsequent support [3]).

Scientific literature<br>[edit]

Initial research and statistical approach<br>[edit]

Initial studies using observational data by Christakis and Fowler suggested that a variety of attributes (like obesity,[4] smoking,[5] happiness,[6][7] loneliness,[8] and alcohol consumption[9]), rather than being individualistic, are casually correlated by contagion mechanisms that transmit such phenomena over long distances within social networks.[10]

Certain subsequent analyses explored limitations to these analyses (subject to different statistical assumptions);[11] or expressed concern that the statistical methods employed in these analyses could not fully control for other environmental factors;[12] or noted that the statistical estimates arising from some approaches may not always have straightforward interpretations;[13] or argued that the statistical methods may not always account for homophily processes in the creation and retention of relationships over time.[14][15]

But other scholarship using sensitivity analysis found that the basic estimates regarding the transmissibility of obesity and smoking cessation, for example, are quite robust,[16][17] or otherwise replicated or supported the findings,[18][19] e.g., in the case of alcohol consumption.[20] Additional, early, detailed modeling work showed that the generalized estimating equation (GEE) modeling approach used by Christakis and Fowler (and other groups) was quite effective for estimating social contagion effects and in distinguishing them from homophily;[21] this paper concluded, "For network influence, we find that the approach appears to have excellent sensitivity, and quite good specificity with regard to distinguishing the presence or absence of such a 'network effect,' regardless of whether or not homophily is present in network formation." Another methodological paper concluded that it is indeed possible to bound estimates of peer effects even given the modeling constraints faced by Christakis and Fowler [19]—even if parametric assumptions are...