Social Proof – 1968
Nearly eighty years after Gabriel Tarde’s ruminations about the society of imitation, a research team headed by the social psychologist Stanley Milgram set up an experiment intended to better understand how social influence spreads through the urban crowd. Mirroring to some extent Tarde’s late-nineteenth-century interest in how imitative contagions propagate through social collectives mostly unawares, Milgram’s experiment in 1968 was designed to stimulate the imitative behaviours of individuals as they encountered a crowd. To begin with, an actor was planted on a busy Manhattan street corner and told to look up at a tall building while the researchers observed the actions of unwitting passers-by. A few of the passers-by noticed and looked up too. However, Milgram then increased the number of skyward looking actors to five. The idea was to gauge how this increase in stimulus would influence the decisionmaking processes of the urbanite passers-by and to record how many more of them would subsequently imitate the skyward looking crowd. In the first test, 20 percent of the passers-by looked up, but when five actors appeared on the street corner, the number apparently jumped to 80 percent. From these results, Milgram deduced his theory of social proof; that is, on encountering the crowd, the individual makes a contagious assumption based on the quantity of evidence that there is something worth looking up at. To put it another way, the individual’s imitation of others is largely dependent on his cognitive assessment of the magnitude of social influence.
Learning from Network Conspiracy
IOCOSE’s latest project A Crowded Apocalypse is an interesting variation on Milgram’s manipulations of imitative crowd behaviour. In this work a crowdsourcing platform is used to assemble a crowd in order for it to spread its own conspiracy and then protest against its protagonists and effects. As the artists explain the project:
“The workers, commissioned through a crowdsourcing platform, are given exact instructions on what to do, and are not required to commit to the cause. They are instead rewarded with a small amount of money (from $1 to $3 max.). There is no ‘ethos’ in the action of the net-workers. While reducing themselves to ‘artificial intelligence’ (as Amazon Mechanical Turk defines crowdsourcing) they transform a practice of activism into a mechanical process.”
Marc Garrett (co-founder of Furtherfield Gallery where the work is currently being shown) asks if it is anticipated that the project will succeed in introducing to the world new conspiracy memes.
“This might be the case, although we should not be too naive in this… The conspiracies we have generated are completely deprived of the political investigation which encourages some (maybe only a few) of the conspiracy theorists out there… The result is a collection of singular, anonymous protests, which slogans and claims, generated through a series of fragmented tasks, barely makes sense. The workers, and the people around them, appear at the same time as victims and beneficiaries, actors and spectators of network technologies… As such, we can imagine their images to become a ‘meme’, as it happened for example to our previous project Game Arthritis or Sokkomb, where the pictures have widely circulated outside of the original context we proposed. They could also become generative of actual protests. We can’t foresee what is going to happen. However, in the context of A Crowded Apocalypse, the people we have involved are not protesters. They are workers.”
It’s interesting to see how these efforts to manipulate crowd contagion are still regarded as memetic. One wonders what neo-Darwinian forces are at work in this wonderful piece of trickery? Even if the meme is rather loosely applied as a way to describe spreading phenomena in general, it is still a rather crude shorthand term for something that is far more deserving of a thoroughgoing expression of social virality. After all, this project seems to be a fascinating addition to contagion theory in the context of crowds and networks. I also find the intentional blurring of the worker/protester role to be intriguing.
Memes aside, it is important to grasp the considerable impact of Milgram’s work on the new network sciences approach to contagion. The popular network contagion models presented by Duncan Watts and Albert-László Barabási e.g. all firmly nod in Milgram’s direction. But social proof is not without its problems too. As I argue in Virality and in a forthcoming chapter with Jussi Parikka, not only has his work greatly influenced current contagion modeling but his ideas figure writ large in the stress given to an individual’s instinctual tendency to herd or cascade, particularly in times of bubble building and subsequent financial crisis but also during the spreading of fashion and fads. In many of these accounts, imitative decisions (rational or irrational) conforming to the social actions of others are assumed to be biologically hardwired into the brain of an individual, enabling a person to make snap judgments to avoid, for example, threats to her physical, emotional, or financial well-being. Notably, even when using online systems like e-mail, it is argued that “the human brain is hardwired with the proclivity to follow the lead of others” (Barton, 2009).
Here I think both Tarde and IOCOSE’s latest project have something far more appealing to say about crowd contagion than memes or social proof, particularly in terms of a social theory in which molar individuals or biologically hardwired gene-memes are not the starting point of analysis, but instead we begin with the vital (and invisible) force of encounter (manipulated or not) occurring in what is assembled (the network or the crowd). Tarde certainly provides an intriguing alternative.
Marc Garrett’s interview with IOCOSE here:
http://andfestival.org.uk/blog/iocose-garrett-interview-furtherfield
The Furtherfield Gallery here: http://www.furtherfield.org/programmes/exhibition/invisible-forces
April Mara Barton, “Application of Cascade Theory to Online Systems: A Study of Email and Google Cascades,” Minnesota Journal of Law, Science, and Technology 10, no. 2 (2009): 474.
Stanley Milgram, Leonard Bickman, and Lawrence Berkowitz, “Note on the Drawing Power of Crowds of Different Size,” Journal of Personality and Social Psychology 13, no. 2 (1969): 79–82.
Tony D Sampson and Jussi Parikka, “Learning from Network Dysfunctionality: Accidents, Enterprise and Small Worlds of Infection.” The Blackwell Companion to New Media Dynamics, Hartley, Burgess and Bruns (eds.), Wiley-Blackwell, (forthcoming, 2012).
VIRALITY 