The Whole Is Always Smaller Than Its Parts

In this paper we argue that the new availability of digital data sets allows one to revisit Gabriel Tarde’s (1843–1904) social theory that entirely dispensed with using notions such as individual or society. Our argument is that when it was impossible, cumbersome or simply slow to assemble and to navigate through the masses of information on particular items, it made sense to treat data about social connections by defining two levels: one for the element, the other for the aggregates. But once we have the experience of following individuals through their connections (which is often the case with profiles) it might be more rewarding to begin navigating datasets without making the distinction between the level of individual component and that of aggregated structure. It becomes possible to give some credibility to Tarde’s strange notion of ‘monads’. We claim that it is just this sort of navigational practice that is now made possible by digitally available databases and that such a practice could modify social theory if we could visualize this new type of exploration in a coherent way.

LATOUR, Bruno, JENSEN, Pablo, VENTURINI, Tommaso, GRAUWIN, Sébastian and BOULLIER, Dominique, 2012. ‘The whole is always smaller than its parts’ – a digital test of Gabriel Tardes' monads. The British Journal of Sociology. December 2012. Vol. 63, no. 4, p. 590–615. DOI 10.1111/j.1468-4446.2012.01428.x. pdf

The digital availability of profiles deeply modifies the definition of what it an individual agent and, reciprocally, what is a structure because of the new ways in which researchers navigate database. This is true not only for human actors but for any agent on which individualizing items are accessible (ants, baboons, as well as bacteria or scientific papers). Building on actor-network theory, physics of complex systems and the visualizing work of Sciences Po médialab, the paper resuscitates the notion of monads that Gabriel Tarde had brought forward and that had disappeared through lack of efficient data tracing tools.