Three degrees of influence

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. Christakis and Fowler found that social networks have great influence on individuals' behavior. But social influence does not end with the people to whom a person is directly tied. We influence our friends, who in their turn influence their friends, meaning that our actions can influence people we have never met. They posit that "everything we do or say tends to 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".

This argument is basically that peer effects need not stop at one degree, and that, if we can affect our friends, then we can (in many cases) affect our friends' friends, and so on. However, across a broad set of empirical settings, the effect seems to no longer be meaningful at a social horizon of three degrees. Christakis and Fowler examined phenomena from various domains, such as obesity, happiness, cooperation, and politics; investigations by other groups have explored many other phenomena in this way.

Mechanism

The influence of actions ripples through networks three degrees (to and from your friends’ friends’ friends). Influence dissipates after three degrees for three reasons, Christakis and Fowler propose:[2]

  1. Intrinsic decay - corruption of information (like the game telephone).
  2. Network instability - social ties become unstable (or are not constant across time) at more than three degrees of separation.
  3. Evolutionary purpose - we evolved in small groups where everyone was connected by three degrees or fewer (an idea receiving subsequent support [3]).

Scientific literature

Studies by Christakis and Fowler suggested that a variety of attributes—like obesity,[4] smoking,[5] and happiness[6]—rather than being individualistic, are casually correlated by contagion mechanisms that transmit these behaviors over long distances within social networks.[7] While certain subsequent analyses suggested limitations to these analyses (subject to different statistical assumptions);[8] or expressed concern that the Christakis-Fowler analyses did not fully control for other environmental factors;[9] or misinterpreted statistical estimates;[10] or did not fully account for homophily processes in the creation and retention of relationships over time;[11][12] other scholarship using sensitivity analysis has found that the core findings regarding the transmissibility of obesity and smoking cessation are robust,[13][14] or has otherwise replicated or supported the findings.[15][16]

Christakis and Fowler reviewed critical and supportive findings in 2013.[14] Moreover, a 2012 paper by physicists ver Steeg and Galstyan suggests it may be possible to bound estimates of peer effects [16] even if parametric assumptions are otherwise required to identify such effects using observational data (if indeed substantial unobserved homophily is thought to be present).[12] Additional support for the modeling approach used by Christakis and Fowler provided by other authors has continued to appear,[17] including of the three-degrees-of-influence property.[18] And additional analytic approaches to observational data have also been supportive, including matched sample estimation,[19] and reshuffling techniques (validating the edge directionality test proposed by Christakis and Fowler in their 2007 paper).[20] The three degrees of influence property has also been observed in criminal networks.[21]

In addition, subsequent studies (by many research groups, including Christakis and Fowler) have found strong causal evidence of behavioral contagion processes (including those that spread beyond dyads, out to two, three, or four degrees) using randomized controlled experiments.[22][23][24][25][26] including one experiment involving 61,000,000 people that showed spread of voting behavior out to two degrees of separation.[27] A 2014 paper also confirmed the spread of emotions online, using another massive experiment.[28] Another experimental study evaluated the spread of risk perception, and documented inflection at approximately three degrees.[29]

Moral implications

The idea of network influence raises the question of free will, because it suggests that we are influenced by factors which we cannot control and which we are not aware of. Christakis and Fowler claim (in their book, Connected) that society should use the knowledge about social networks in order to create a better society with a more efficient public policy. This applies to many aspects of life, from public health to economics. For instance, they note that it might be preferable to immunize individuals located in network's center more than peripheral individuals. Or, it might be much more effective to motivate clusters or people to avoid criminal behavior than to act upon individuals or than to punish each criminal separately.

If people are connected to everyone by six degrees of separation (according to the social psychologist Stanley Milgram) and influence those up to three degrees (Christakis and Fowler), then people can reach halfway to anyone in the world.[30]

See also

References

  1. ↑ "The hidden influence of social networks Nicholas Christakis on TED.com".
  2. ↑ Connected Preface+chapter1
  3. ↑ Morgan, TJH; et al. (2015). ", Experimental evidence for the co-evolution of hominin tool-making teaching and language". Nature Communications 6: 6029. doi:10.1038/ncomms7029.
  4. ↑ Christakis, Nicholas A.; Fowler, James H. (2007). "The Spread of Obesity in a Large Social Network over 32 Years". The New England Journal of Medicine 357 (4): 370–379. doi:10.1056/NEJMsa066082. PMID 17652652.
  5. ↑ Christakis, Nicholas A.; Fowler, James H. (2008). "The Collective Dynamics of Smoking in a Large Social Network". The New England Journal of Medicine 358: 2249–2258. doi:10.1056/NEJMsa0706154. PMC 2822344. PMID 18499567.
  6. ↑ Christakis, Nicholas A.; Fowler, James H. (2008). "Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study". British Medical Journal 337 (337): a2338. doi:10.1136/bmj.a2338. PMC 2600606. PMID 19056788.
  7. ↑ Christakis, Nicholas A.; Fowler, James H. (2009). Connected:The Surprising Power of Our Social Networks and How They Shape Our Lives. Little, Brown and Co. ISBN 978-0316036146.
  8. ↑ Cohen-Cole, Ethan; Fletcher, Jason M. (2008). "Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis". British Medical Journal 337: a2533. doi:10.1136/bmj.a2533.
  9. ↑ Cohen-Cole, Ethan; Fletcher, Jason M. (2008). "Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic". Journal of Health Economics 27: 1382–1387. doi:10.1016/j.jhealeco.2008.04.005.
  10. ↑ Lyons, Russell (2011). "The Spread of Evidence-Poor Medicine via Flawed Social Network Analysis". Statistics, Politics, and Policy 2 (1). doi:10.2202/2151-7509.1024.
  11. ↑ Noel, Hans; Nyhan, Brendan (2011). "The 'unfriending problem': The consequences of homophily in friendship retention for causal estimates of social influence". Social Networks 33 (3): 211–218. doi:10.1016/j.socnet.2011.05.003.
  12. 1 2 Shalizi, Cosma R.; Thomas, Andrew C. (2011). "Homphily and Contagion Are Generically Confounded in Observational Social Network Studies". Sociological Methods & Research 40 (2): 211–239. doi:10.1177/0049124111404820.
  13. ↑ VanderWeele, Tyler J. "Sensitivity Analysis for Contagion Effects in Social Networks". Sociological Methods & Research 40 (2): 240–255. doi:10.1177/0049124111404821.
  14. 1 2 Christakis, NA; Fowler, JH (2013). "Social Contagion Theory: ExaminingDynamic Social Networks and Human Behavior". Statistics in Medicine 32: 556–577. doi:10.1002/sim.5408.
  15. ↑ Ali, MM; Amialchuk, A; Gao, S; Heiland, F (2012). "Adolescent Weight Gain and Social Networks: Is There a Contagion Effect?". Applied Economics 44: 2969–2983. doi:10.1080/00036846.2011.568408.
  16. 1 2 Steeg, A. Galstyan (2012). "Statistical Tests for Contagion in Observational Social Network Studies". Journal of Machine Learning Research: 563–571.
  17. ↑ Gonzalez-Pardo, A.; Cajias, R.; Camacho, D. (2014). "An Agent Based Simulation of Christakis-Fowler Social Model". Recent Developments in Computational Collective Intelligence 513: 69–77.
  18. ↑ http://www.cbma.bio.uminho.pt/files/Pacheco-Manuscript.pdf
  19. ↑ Aral, Sinan; Muchnik, Lev; Sunararajan, Arun (2009). "Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks". Proceedings of the National Academy of Sciences 106 (51): 21544–21549. doi:10.1073/pnas.0908800106.
  20. ↑ Anagnostopoulos, Aris; Kumar, Ravi; Mahdian, Mohammad (2008). "Influence and Correlation in Social Networks". Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: 7–15. doi:10.1145/1401890.1401897.
  21. ↑ Wildeman, Christopher; Papachristos, Andrew V. "Network Exposure and Homicide Victimization in an African American Community". American Journal of Public Health 104 (1): 143–150. doi:10.2105/ajph.2013.301441.
  22. ↑ Centola, Damon (2010). "The Spread of Behavior in an Online Social Network Experiment". Science 329 (5995): 1194–1197. doi:10.1126/science.1185231.
  23. ↑ Centola, Damon (2011). "An experimental study of homophily in the adoption of health behavior". Science 334 (6060): 1269–1272. doi:10.1126/science.1207055.
  24. ↑ Fowler, James H.; Christakis, Nicholas A. (2010). "Cooperative behavior cascades in human social networks". Proceedings of the National Academy of Sciences 107 (12): 5334–5338. doi:10.1073/pnas.0913149107. PMC 2851803. PMID 20212120.
  25. ↑ Aral, Sinan; Walker, Dylan (2011). "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks". Management Science 57 (9): 1623–1639. doi:10.1287/mnsc.1110.1421.
  26. ↑ Rand D, Arbesman S, and Christakis NA,"Dynamic Social Networks Promote Cooperation in Experiments with Humans," PNAS:Proceedings of the National Academy of Sciences 2011; 108: 19193-19198
  27. ↑ Bond, RM; Fariss, CJ; Jones, JJ; Kramer, ADI; Marlow, C; Settle, JE; Fowler, JH (2012). "A 61-million-person experiment in social influence and political mobilization". Nature 489: 295–298. doi:10.1038/nature11421.
  28. ↑ Kramer, ADI; Guillory, JE; Hancock, JT (2014). "Experimental evidence of massive-scale emotional contagion through social networks" (PDF). Proceedings of the National Academy of Sciences 111: 8788–8790. doi:10.1073/pnas.1320040111. PMID 24889601.
  29. ↑ Moussaid, M; Brighton, H; Gaissmaier, W (2015). "The amplification of risk in experimental diffusion chains" (PDF). Proceedings of the National Academy of Sciences 112: 5631–5636. doi:10.1073/pnas.1421883112.
  30. ↑ connectedthebook.com - Download slides

External links

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