Crowd Control: Social Machines and Social Media
The philosopher’s stone of the Network’s age of social media is crowd control. The algorithmic businesses popping up on the sides of the information superhighway require a reliable input if their algorithms are to reliably output saleable goods. And it’s crowdsourcing that has been given the task of providing the dependable processed input. We assign a piece of the process to no one in particular and everyone in general via software on the Network. The idea is that everyone in general will do the job quicker, better and faster than someone in particular. And the management cost of organizing everyone in general is close to zero, which makes the economics of this rig particularly tantalizing.
In thinking about this dependable crowd, I began to wonder if the crowd was always the same crowd. Does the crowd know it’s a crowd? Do each of the individuals in the crowd know that when they act in a certain context, they contribute a dependable input to an algorithm that will produce a dependable output? Does the crowd begin to experience a feedback loop? Does the crowd take the algorithm’s dependable output as an input to its own behavior? And once the crowd has its own feedback loop, does the power center move from the algorithm to the crowd? Or perhaps when this occurs there are two centers of power that must negotiate a way forward.
We speak of social media, but rarely of social machines. On the Network, the machine is virtualized and hidden in the cloud. For a machine to operate at full efficiency, each of its cogs must do its part reliably and be able to repeat its performance exactly each time it is called upon. Generally some form of operant conditioning (game mechanics) will need to be employed as a form of crowd control. Through a combination of choice architecture and tightly-defined metadata (link, click, like, share, retweet, comment, rate, follow, check-in), the behavior of individuals interacting within networked media channels can be scrapped for input into the machine. This kind of metadata is often confused with the idea of making human behavior intelligible to machines (machine readable). In reality, it is a replacement of human behavior with machine behavior— the algorithm requires an unambiguous signal (obsessive compulsive behavior).
The constraints of a media are vastly different than those of a machine. Social media doesn’t require a pre-defined set of actions. Twitter, like email or the telephone, doesn’t demand that you use it in a particular way. As a media, its only requirement is that it carry messages between endpoints— and a message can be anything the medium can hold. Its success as a media doesn’t rely on how it is used, but that it is used.