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Nathan Yang selected participant in joint Quebec and China seminar

Published: 23 July 2018

Nathan Yang, Assistant Professor in Marketing, selected participant in joint Quebec and China seminar Big Data and Management.

As part of the collaborative agreement between the Fonds de recherche du Québec – Société et culture (FRQSC) and the National Natural Science Foundation of China (NSFC), a delegation of Québec researchers will take part in a seminar on the use of big data in management science in Nanjing, China, from September 18 to 20, 2018.

Selected as one of 40 researchers from around the world to participate, Professor Nathan Yang of the Desautels Faculty of Management will be travelling to Nanjing as a representative of GERAD (Group for Research in Decision Analysis - a multi university research center), to present his ongoing and recently published research on the topic of the intersection between behavioural science and big data. 

Summary of subject

Behavioural scientists have demonstrated the power of the nudge, as past research has identified subtle marketing strategies that could markedly change behaviour. At the same time, the development of big data analytics and artificial intelligence has advanced at an incredibly fast rate (e.g., prediction, causal inference using machine learning, recommendation systems and personalization). However, these innovation clusters have been advanced largely independently of one another, so I see opportunities to blend together these clusters. This proposed integration I see as value-creating, as big data and artificial intelligence will help researchers quantify the effectiveness of nudges over a long period of time, potentially generalize nudges to impact a larger population, and ultimately scale-up the nudges so that they can be implemented in real-time. At the same time, behavioural science researchers will add discipline in the development of artificial intelligence techniques, as their past insights will provide important priors about how the training samples should be collected and how best to evaluate the face-validity of AI-generated predictions. This topic would be related to the mission's sub-theme around business, and more specifically, in consumer marketing.

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