Social sciences often draw on other disciplines (e.g psychology, statistics, biology...) techniques for quantitative analysis which open up new perspectives of research. For instance, Nancy Tuma took duration models from biology and industrial sciences, while Harrison White derived blockmodeling from physics. However, scholars do not necessarily - or not evenly – take these methodological borrowings up. Some techniques never find an audience, while others are used for something they weren’t meant for.
Optimal Matching Analysis offers an example of a relatively successful importation. Commonly used in bio-informatics for the study of DNA strings, OMA is an algorithm measuring dissimilarity between sequences, which mostly intends to explore and identify patterns among sets of diachronic data. It allows to summarize the timing and sequence of events, durations in the various states and durations between events. Contrary to event history analysis, it is non-parametric: OMA makes no assumption about the process underlying sequences unfolding and thus it belongs to the algorithmic model culture.
Since it was introduced in social sciences by Andrew Abbott during the 80's, its use has dramatically spread, particularly during the last ten years. Still, two distinct periods may be distinguished. The first one, from 1986 to 2000, rests almost exclusively on Abbott’s efforts to promote its method, while the second one, from 2000, sees a significantly growing appropriation among social science scholars and the autonomization of OMA use from Abbott’s works. This presentation aims at exploring the circumstances which allowed this process. We successively examine OMA position in the space of methods (e.g. the opposition between descriptive and causal methods); Abbott's position and that of OMA users (who are often newcomers) in the scientific field; the existence of communication networks of diffusion, such as workshops or ‘OMA-friendly’ journals; the special issue of Sociological Methods and Research in 2000 as a potential ‘turning point’; technical means to apply OMA, through the implementation of specific modules in statistics softwares. More generally, we propose avenues to think about the social conditions of the diffusion of new methods in social sciences.