Digital technologies and services are shaping our lives. Work, education, finance, health, politics and society are all affected. They also raise concomitant and complex challenges relating to the security of and trust in systems and data. TIPS (Trust, Identity, Privacy and Security) issues thus lie at the heart of our adoption of new technologies and are critical to our economic prosperity and the well-being of our citizens. Identifying and addressing such issues requires a coherent, coordinated, multi-disciplinary approach, with strong stakeholder relationships at the centre. SPRITE+ is a vehicle for communication, engagement, and collaboration for people involved in research, practice, and policy relevant to TIPS in digital contexts. Since launching in 2019, we have established ourselves as the go-to point of contact to engage with the broadest UK network of interdisciplinary, cross-sector digital TIPS experts. The second phase of SPRITE+ ('SPRITE+2') will continue to build our membership, whilst expanding the breadth and depth of our innovation, and deepen our impact through proactive engagement. SPRITE+2 will have the following objectives: 1. Expand our TIPS community, harnessing the expertise and collaborative potential of the national and international TIPS communities 2. Identify and prioritise future TIPS research challenges 3. Explore and develop priority research areas to enhance our collective understanding of future global TIPS challenges 4. Stimulate innovative research through sandpits, industry led calls, and horizon scanning 5. Deepen engagement with TIPS research end users across sectors to accelerate knowledge Exchange 6. Understand, inform, and influence policy making and practice at regional, national and international level These will be delivered through four work packages and two cross cutting activities. All work packages will be led by the PI (Elliot) to ensure that connections are made and synergies exploited. Each sub-work package will be led by a member of the Management Team and supported by our Expert Fellows and Project Partners. WP1 Develop the Network We will deliver a set of activities designed to expand, broaden, and engage the network, from expert meetings and workshops to student bootcamps and international conferences. WP2 Engage stakeholders to enhance knowledge exchange and deliver impact. We will be greatly enhancing our purposive engagement activity in SPRITE+2. This activity will include a new business intelligence function and PP engagement grants, designed to enhance mutual understanding between researchers and stakeholders. WP3 Identify, prioritise, and explore future TIPS challenges We will select and then investigate priority areas of future TIPS. Two areas are pre-scoped based on the work we have done so far in SPRITE+ (TIPS in digital cities; trustworthy digital identities) with a further two be identified during the lead up to SPRITE+2. WP4 Drive innovation in research This WP concerns the initiation and production of high-quality impactful research. Through horizon scanning, sandpits and industry-led calls, we will steer ideas through an innovation pipeline ensuring SPRITE+2 is future focused. Cross cutting activities The first cross-cutting activity will accelerate the translation of TIPS research into policy and practice for public and private sector end uses. The second focuses on mechanisms to facilitate communication within our community. The experiences of SPRITE+ and the other DE Network+s demonstrate that it takes years of consistent and considerable effort for a new network to grow membership and develop productive relationships with stakeholders. In SPRITE+2 grant we would hit the ground running and maximise the impact of four additional years of funding. A successful track record, a well-established team, and a raft of ambitious new plans provide a solid foundation for strong delivery in 2023-27.

National infrastructure provides essential services to a modern economy: energy, transport, digital communications, water supply, flood protection, and waste water / solid waste collection, treatment and disposal. The OECD estimates that globally US$53 trillion of infrastructure investment will be needed by 2030. The UK's National Infrastructure Plan set out over £460 billion of investment in the next decade, but is not yet known what effect that investment will have on the quality and reliability of national infrastructure services, the size of the economy, the resilience of society or its impacts upon the environment. Such a gap in knowledge exists because of the sheer complexity of infrastructure networks and their interactions with people and the environment. That means that there is too much guesswork, and too many untested assumptions in the planning, appraisal and design of infrastructure, from European energy networks to local drainage systems. Our vision is for infrastructure decisions to be guided by systems analysis. When this vision is realised, decision makers will have access to, and visualisation of, information that tells them how all infrastructure systems are performing. They will have models that help to pinpoint vulnerabilities and quantify the risks of failure. They will be able to perform 'what-if' analysis of proposed investments and explore the effects of future uncertainties, such as population growth, new technologies and climate change. The UK Infrastructure Transitions Research Consortium (ITRC) is a consortium of seven UK universities, led by the University of Oxford, which has developed unique capability in infrastructure systems analysis, modelling and decision making. Thanks to an EPSRC Programme Grant (2011-2015) the ITRC has developed and demonstrated the world's first family of national infrastructure system models (NISMOD) for analysis and long-term planning of interdependent infrastructure systems. The research is already being used by utility companies, engineering consultants, the Institution of Civil Engineers and many parts of the UK government, to analyse risks and inform billions of pounds worth of better infrastructure decisions. Infrastructure UK is now using NISMOD to analyse the National Infrastructure Plan. The aim of MISTRAL is to develop and demonstrate a highly integrated analytics capability to inform strategic infrastructure decision making across scales, from local to global. MISTRAL will thereby radically extend infrastructure systems analysis capability: - Downscale: from ITRC's pioneering representation of national networks to the UK's 25.7 million households and 5.2 million businesses, representing the infrastructure services they demand and the multi-scale networks through which these services are delivered. - Upscale: from the national perspective to incorporate global interconnections via telecommunications, transport and energy networks. - Across-scale: to other national settings outside the UK, where infrastructure needs are greatest and where systems analysis represents a huge business opportunity for UK engineering firms. These research challenges urgently need to be tackled because infrastructure systems are interconnected across scales and prolific technological innovation is now occurring that will exploit, or may threaten, that interconnectedness. MISTRAL will push the frontiers of system research in order to quantify these opportunities and risks, providing the evidence needed to plan, invest in and design modern, sustainable and resilient infrastructure services. Five years ago, proposing theory, methodology and network models that stretched from the household to the globe, and from the UK to different national contexts would not have been credible. Now the opportunity for multi-scale modelling is coming into sight, and ITRC, perhaps uniquely, has the capacity and ambition to take on that challenge in the MISTRAL programme.

Large scale black box statistical models are ubiquitous in modern society; and aimed at providing a way to examine the behaviour of complex systems. For example, Improbable has helped design such models as part of the RAMP initiative to help the UK government predict the spread of the COVID-19 virus. In engineering, so-called 'digital twins' of real-world physical phenomena or assets are commonly used to conduct virtual stress tests and predict the behaviour of critical systems in the presence of exogenous shocks. An important concern for these models is the nature of our uncertainty about their predictions and recommendations. Unlike for more traditional statistical analysis, the underlying models are often highly complex, not immediately interpretable, and often misspecified. As a consequence, standard Bayesian methods of uncertainty quantification derived under the assumptions of the traditional paradigm for statistical analysis are often inappropriate. More specifically, they often result in over-confidence and a lack of robustness. To tackle this issue, generalised forms of Bayesian uncertainty quantification have recently been developed. Such methods can ensure robustness and reduce the computational burden relative to standard Bayesian methods. This makes them ideal for applications in simulation-based modelling scenarios---such as COVID-19 modelling or digital twins. Yet, to date they have not been used in this context and primarily enjoyed success in time-ordered problems (such as on-line learning, changepoint detection, or filtering and smoothing) as well as in Bayesian Deep Learning applications (such as Bayesian neural networks or deep Gaussian Processes). In spite of their promise however, both their foundational theoretical properties as well as their computation are under-explored topics of research. In this fellowship, I will advance the theory, methodology, and application of generalised Bayesian posteriors that are defined implicitly through an optimisation problem. While such generalised Bayesian methods have shown great promise, a thorough investigation of this kind will be required if they are to be adopted more widely. As part of this, I will investigate the fundamental question of how one should choose between different generalised posteriors. Complementing this, I will devise methodology for Bayesian computation geared towards the special properties of these posteriors. I will then leverage the advances made as part of this research to apply them on two classes of high-impact problems that traditional Bayesian methods struggle with: models revolving around intractable likelihoods, and simulator-based inference. For the applied component of this research programme, I will draw on the expertise of my project partners and use generalised posteriors for better uncertainty quantification in 'digital twins', as well as applications of importance for national security---such as modelling the COVID-19 pandemic.