My research addresses how new governmental practices emerge on the basis of the alliance between new data gathering capacities and innovative machine-learning techniques. Examples include marketing profiling, automated surveillance, high-frequency trading. Entitled “Computing Economics: at the crossroads of scientific, technical and socio-economic normativities”, my dissertation is intended to problematize the relationships that computer scientists establish with algorithms, as well as to pinpoint the scientific and political effects these algorithms might produce when connected to commercial, financial or economic modeling. In order to analyze this, I have selected several case studies belonging to distinct but related fields of computer science (predominantly linear programming and metaheuristics) in order to capture some sense of the range of current algorithmic practices to document and to conceptualize the change they effect upon statistical, economical and governmental practices. More precisely, my dissertation addresses two main questions: i) How do computer scientist relate to their algorithms? ii) How did the algorithm emerged as an object of knowledge?

Histoire et philosophie des sciences et des techniques (HPST)

Science and Technology Studies (STS)

Philosophie politique

Bachelier en Philosophie (Magna Cum Laude)

Maîtrise en Philosophie (Magna Cum Laude)