AI based technology adoption in circular economics

This project is a cross-disciplinary study of econometrics and machine learning (ML) models applied to the decision making modelling in industry. The problematic arises from the lack of tools supporting the transition to circular economics model and the need to identify the key factors to influence this transition.

The project aims to explore the key elements affecting the high management decision making process in technology adoption. A discrete choice experiment survey will be conducted. The data analysis procedure will involve both econometrics and machine learning techniques. Canadian partners will provide the information sources in industrial domain, as well as the knowledge in advanced ML and AI modelling techniques. French side posesses all the required knowledge and skills for analysis of human behaviour. The simulation and theory-testing framework proposed in previous works, will constitute the core of modelling approach, allowing to increase the reliability of results.

Faculty Supervisor:

Bruno Agard

Student:

Partner:

Université Grenoble Alpes

Discipline:

Computer science

Sector:

Education

University:

Polytechnique Montréal

Program: