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.

Personal data holds great potential to benefit commerce and society, but, at the institutional level, concerns are rising over the risks associated with data access, ownership, privacy and confidentiality. The main purpose of this project is to investigate whether and how these institutional concerns are reflected in the perceptions of individual users. This proposal will establish a new programme of research in digital economy by understanding how individual subjective perceptions of users with regard to cybersecurity relate to organizational and institutional views on cybersecurity. By gaining this understanding we seek to develop new business models which would allow businesses to minimize individual perceptions of vulnerability with regard to issues of privacy, security, and trust. We propose that individual subjective vulnerability with regard to cybersecurity issues is an important factor which impact upon business models and the development of digital economy. We consider vulnerability from three perspectives: 1) An individual's perspective of their own vulnerability; 2) The perspective of the entity the individual is interacting with in the digital domain (which could be another individual, or a business); and 3) The institution that is tasked to regulate and protect all entities within the system (e.g., the state, regulatory body, etc.). All three entities are likely to assess individual vulnerabilities in different ways and would have a separate sets of trade-offs against the risks. An individual considers the trade-off between the choice/freedom to use a service against the risk of being vulnerable. A business, on the other hand, approximates individual's vulnerability and makes an assessment of risk which is important for its business model in order to trade off revenues and provide additional service to mitigate that risk to the extent that it would pacify the user and the regulator. Finally, from a state point of view, the aggregation of a large numbers of users creates a complex system of data sharing which bears a systemic risk that may result in individual vulnerabilities which are hard to quantify and manage. Proposed project will implement "in-the-wild" strategy in order to: (i) Measure individual vulnerability with regard to cybersecurity issues using different contexts and taking into account individual heterogeneity; (ii) Using these context-dependent measure, propose new business models which would mitigate perceptions of cybersecurity risks; (iii) Suggest tools for policy makers and regulators to decrease cybersecurity risks via bridging the gap between subjective vulnerability of users and objective vulnerability measured by businesses and other institutions.