Stay Happy: Understanding Urban Wellbeing Using a Behavioural Machine Learning Approach

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Recent advances in information and communication technology (ICT) have created opportunities for the public sector to increase citizens' wellbeing by better understanding their preferences. Machine learning, a subfield of computer science, is a valuable tool to understand consumer/user preferences, including those relevant to urban development and urban service provision. Indeed, it can help to create new urban value propositions by applying data-driven approaches to urban business modelling. Machine learning algorithms in the urban domain provide valuable insights to policymakers. While the existing methodology produces models that can explain some data well, it can be improved by incorporating mathematical modelling from Decision and Behavioural Science to better capture the behavioural component of urban wellbeing. Research in behavioural science offers models of individual decision-making that generate accurate predictions, and yet these models are not usually applied to large datasets; they are used on small amounts of data obtained through decision-making experiments. Understanding how behavioural science models can enrich existing machine learning to improve the personalisation of urban services - and by doing so increase citizen satisfaction - is the focus of the proposed research programme.Our programme will address urban wellbeing through: an innovative approach to data analytics, Behavioural Machine Learning (BML); the creation of new urban policies as a result of improved personalisation; and the interaction with data (including self-generated data by citizens as well as citizen data by businesses and policymakers) in day-to-day decision-making. The proposed research involves large datasets collected from field experiments as well as publicly available data, the former particularly focused on the complex and large datasets that capture individual citizen characteristics and wellbeing.Our programme is innovative because it: (a) broadens and integrates research in behavioural science, data analytics, computer science, and human-data interaction (HDI); (b) examines decision design for complex data-driven urban decisions that involve datasets with low informativeness, very large datasets which are difficult to manage, and noisy data; and (c) focuses on how the data is used by citizens, businesses, and policymakers.It will have a broad impact in several ways. Firstly, we will work directly with multiple stakeholders to generate solutions that have practical implications for creating new urban policies in practice with measurable benefits. Secondly, its research outputs will suggest improved participatory modes for analysis and presentation of different types of data in the digital economy more generally. Its third value is methodological, as we offer a model of close collaboration and integration for social and natural sciences research. As a result of this project, qualitative and quantitative researchers will better understand the limits and possibilities of the other's methodology, leading to better and more applicable interdisciplinary research. A final impact is on general education in science: blending social and natural science enriches and motivates students of all ages, as well as the members of the general public.