Résumé (EN)

The main argument of this paper is that, in the social sciences, complexity comes from the fact that actors act if they create meanings for their actions –meanings that help them recognize their action as appropriate. In addition they are able, through learning (both individual, relational and collective), to modify these meanings, re-evaluate the appropriateness of their actions and, subsequently, change the course of their actions. Therefore I argue that the trans-disciplinarity coming from the science of complexity should focus on further modelling phenomena such as collective learning based on such premises. Sociologists have long done just that, using small, complex, and rich datasets –usually not big data and models that they perceive as reifying the object too much. This presentation provides an empirical example combining a sociological theory of appropriateness judgments and the analyses of the dynamics of advice networks in order to model collective learning. The example is based on the observation of the work of judges in a courthouse and on the analysis of the dynamics of their advice network. This phenomenon and this example raise the issue of the place of sociology in the current paradigmatic context of complexity sciences. Further modelling of sense-making, appropriateness judgments and collective learning together with other disciplines would also be crucial for complexity sciences if they want to help explain action by human beings who tend to want to make sense of the world, and by extension what happens in their groups and in society at large.