Behavioural Modelling of Space and Time in the City Context: Implications for Urban Wellbeing

Current research in economics and psychology shows that people of different ages perceive time and space differently. This project will systematically measure those differences and relate them to urban wellbeing. The research will use data inference and machine learning algorithms to develop ways in which end users (citizens, households, policy-makers, and businesses) can change their behaviour in order to reach higher levels of urban wellbeing in the city context. Design and conduct laboratory and field experiment will then be used to test how wellbeing data and behavioural insights should be presented to citizens in order to 'nudge' them to make more optimal decisions and to increase urban wellbeing. The responsibilities of the role are to focus on the design and development of: (i) feedback protocols and (ii) incentive mechanisms, informed by the work of the project team on human-computer and human-data interactions; and (iii) evaluation of mechanisms for specifying happiness requirements as well as determining the data flow between the platforms used by the team, partners (SunBath and Databox), and external applications.

Throughout this project an independent programme of research will be developed and I will explore how individual citizens and households could be 'nudged' to make better decisions about their urban environment and lifestyle, in order to increase their wellbeing.