Bootstrapping the Next Generation of Social Machines

Abstract

The term ``social machines{''} denotes a class of systems where humans and machines interact so that computational infrastructure supports human creativity. Flagship examples such as Wikipedia and Ushahidi demonstrate how computational coordination can enhance information sharing and aggregation, while the Zooniverse family of projects show how social machines can produce scientific knowledge. These socio-technical systems cannot easily be analysed in purely computational or purely sociological terms, and they cannot be reduced to Turing machines. Social machines are used in the creation of software, from software crowdsourcing projects such as TopCoder and oDesk, to distributed development platforms such at GitHub and Bitbucket. Hence, social machines are increasingly used to create the software infrastructure for new social machine. However, social machine development is a more complex process than software development, as the community must be ``programmed{''} as well as themachines. This leads to development in the context evolving and unknown requirements, and having to deal with more sociological concepts than formal systems designers usually work with. We hence model the process using two coupled social machines: the target social machine, with whatever purposes the creators envisions, and the development social machine which is used to create it. As an example, oDesk can form part of a development social machine which might be used to create a target social machine, e.g. ``the next Facebook{''}. In this chapter, we describe a formalism for social machines, consisting of i) a community of humans and their ``social software{''} interacting with ii) a collection of computational resources and their associated state, protocols and ability to analyse data and make inferences. We draw on the ideas of `desire lines' and `play-in' to argue that top down design of social machines is impossible, that we hence need to leverage computational support in creating complex systems in an iterative, dynamic and emergent manner, and that our formalism provides a possible blueprint for how to do this.