This proposal is motivated by the need to reduce the public deficit. One way to do this is by achieving efficiency savings in procurement for large public institutions such as the National Health Service, city councils, or the Ministry of Defence. We propose to contribute towards this goal by attempting to better align the stylised theoretical analysis of tendering - a form of trading mechanism - with the facts on the ground. The focus of our study is the provision and use of information in the tendering process, building on two recent methodological developments: "Information Design" and "Simple Auctions". Trading mechanisms have been the subject of a great deal of study, especially in the last half a century. More recently, the enormously successful sale of the 3G mobile phone licences by simultaneous auctions - £22.5 billion was raised for the public purse and the band of radio frequency was efficiently assigned - in 2000, provided vivid evidence of how useful this theory can be. The literature on "auctions" is focussed on finding the optimal trading mechanism, which maximizes expected benefits. However, on the one hand, this optimization assumes that the information available to the bidders is predetermined. This is often too strong an assumption as the bid taker may have significant leeway in choosing what information to gather and disclose. On the other hand, the optimization traditionally leaves both the complexity of the mechanism and its use of the information revealed by the bidders unconstrained. This often results in very complicated "optimal" mechanisms, which are hard to implement in practice. We propose to push out the research frontier by analysing what information, and in which form, is presented to the potential traders and how information revealed by them is used by the designer to determine prices and trades. The first of these novel ideas is information design: the optimal provision of information to a group of interacting agents by a designer with a certain objective. By strategically choosing its method for scoring the bids and by seeking out and revealing additional facts that might affect the cost of suppliers, the designer can create interdependence between the agents' information; this can then be exploited through the competitive bidding process, ultimately benefiting the designer's objective. The second idea is based on the observation that due to the complex objective of the buyer (quality, timing, transparency, sustainability etc. in addition to price) most actual tenders are multi-dimensional: the bids submitted include several different factors besides price. While a pre-announced scoring rule can transform these bids into readily comparable one-dimensional scores, it does not eliminate the complexity of bids and of the bidders' beliefs about the bids of others. For practical reasons, the designer needs to compensate for this innate complication by simplifying the mechanism, resulting in additional restrictions on the set of mechanisms she can choose from. These restrictions imply that families of mechanisms previously discarded as sub-optimal, now become relevant. To capture this scenario, we analyse decentralised mechanisms, where conditional on trading, prices are independent of the bids of competitors. In the context of scoring auctions, this would correspond to a discriminatory "first-score" auction. According to the existing theoretical literature, when the quantity traded is not set beforehand, these auctions are not optimal. Together, these two approaches make it possible to advance our understanding of issues like simultaneous bidding and realistic mechanisms that deal with interdependent valuations. While doing that we will also pay particular attention not to be hemmed in by the artificial boundary between micro- and macro-economic analyses, so that our insights can be exported to system-wide markets, such as the labour and credit markets.

In numerous contexts today we are faced with making decisions of increasing size and complexity, where many different considerations interlock in complex ways. Consider a staff rostering problem to assign staff to shifts while respecting required shift patterns and staffing levels, physical and staff resources, and staff working preferences. The decision-making process is often further complicated by the need also to optimise an objective, such as to maximise profit or to minimise waste. It is natural to characterise such problems as a set of decision variables, each representing a choice that must be made in order to solve the problem at hand (e.g. which staff member is on duty for the Friday night shift), and a set of constraints describing allowed combinations of variable assignments (e.g. a staff member cannot be assigned to a day shift immediately following a night shift). A solution is an assignment of a value to each variable satisfying all constraints. Many decision-making and optimisation formalisms take this general form. In all of these formalisms the model of the problem is crucial to the efficiency with which it can be solved. A model in this sense is the set of decision variables and constraints chosen to represent a given problem. There are typically many possible models and formulating an effective model is notoriously difficult. Therefore automating modelling is a key challenge. Over the last decade, in the context of Constraint Programming we have taken a novel approach to addressing this challenge. The user writes a problem specification in the abstract constraint specification language 'Essence', capturing the structure of the problem above the level of abstraction at which modelling decisions are made. Our modelling pipeline, on which our proposed research is based, automatically generates a model from this specification. This removes the need for user constraint modelling expertise, and also preserves the structure of the specified problem, allowing the system easily to explore alternative models and to exploit properties such as symmetry. Our pipeline generates constraint models equivalent in quality to those of a competent human constraint programmer, and so represents a significant milestone towards fully automated modelling. Important challenges do, however, remain. The first is to generate models of the quality that human experts are capable. Given the inherent difficulty of these problems, and the importance of the model in mitigating that difficulty, raising the quality of the generated models is crucial. The second is to expand the range of output models beyond the constraint programming formalism. The substantial challenge we address in this proposal is to overcome these two limitations to produce a powerful, general automated modelling and solving system unique in targeting a range of solving formalisms from a single abstract constraint specification. Our existing pipeline is ideal for extension to other formalisms. The impact of this change will be substantial: combinatorial search problems are ubiquitous across the public and private sectors, and academia. We will deliver better solutions to these problems more rapidly, increasing efficiency and reducing cost.

THE PROBLEM There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than "health", which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from "health policy" to "healthy public policy". OUR POLICY FOCUS We will work with three policy partners at local, regional and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority and Scottish Government. We will focus on four jointly agreed policy priorities for good health: - Creating a fairer economy - Promoting mental wellbeing - Providing affordable, good quality housing - Preventing long-term effects of difficult childhoods. OUR COMPLEX SYSTEMS SCIENCE APPROACH Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g. housing) can have large unintended effects in others (e.g. poverty). There is often no "correct" solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes is crucial so failing policies can be revised or dropped. We propose to use complex systems modelling, which has been developed to understand and make projections of what might happen in complex systems given different plausible assumptions about future developments. Our models will be underpinned by the best available data and prior research in each policy area. Our new evidence about likely policy effects across a wide range of outcomes will help policy partners decide between alternative policies, depending on how important different outcomes are to them (e.g. improving health or economic growth). We will develop support tools that can visualise the forecasts, identify policies that achieve the desired balance between competing outcomes and update recommendations when new information emerges. Whilst new to public health policy, these methods are well-established in engineering and climate science. We will 1. Work with policy partners to understand the policy systems and evidence needs 2. Bring together existing data and evidence on each policy system (e.g. links between policies and outcomes, interdependencies between outcomes) 3. Explore citizens' preferences for prioritising when not all outcomes can be achieved 4. Link policies and their health and non-health effects in computer models to analyse benefits and costs over time 5. Build an interactive tool to help policy decision-making, inform advocacy action and support political debate. SIPHER's MAIN OUTCOME We will provide policymakers with a new methodology that allows them to estimate the health-related costs and benefits of policies that are implemented outside the health sector. This will be useful to our partners, and others, who want to assess how scarce public sector resources can be spent to maximise the health and wellbeing benefits from all their activities.

Food provided in schools has a major influence on the quality of children's diets and has the potential to reduce inequalities in dietary intake between children according to their social or economic background. The quality of diet in childhood has been shown to impact on future development, educational achievement, health and well-being outcomes, and also influences diet in adulthood, as well as disease risk (e.g. diabetes, heart disease) in later life. There are differences in how schools arrange their food provision and what they serve, between schools and between countries in the UK, and this is not well understood. We propose a UK school food network (GENIUS), considering the food system across the preschool, primary and secondary schools, and including all school food provision, both within and outside the canteen. The aim of this network will be to work towards a more health-promoting food and nutrition system in UK schools. Objectives include the development of a network of academics and non-academics across the UK actively researching and influencing school food, the use of a range of novel research methods to understand the current UK school food system, and appreciate its complexities, and examination of similarities and differences and areas of best practice between the four nations of the UK. Finally, the network will explore opportunities for interventions that will positively impact on school food, improve the diet quality of children at school and reduce inequalities. The network will bring together researchers from a range of backgrounds including nutrition, epidemiology, public health, sensory science, health economics, health informatics, health psychology, education, planning and policy. Inclusion of project partners who are actively involved in the provision of school food from across the UK, including from local government, catering providers, pupils and parents, will make sure the work of the network is immediately useful and, together, this team of academics and non-academics will ensure the-development of research priorities and questions that are relevant in the school setting. This network will use a combination of workshops, working groups and funding of small projects to map the school food system and work together to develop research questions. Understanding the current food system and building this network of those interested in and working in school food will advance research and policy around the food environment in schools. Findings will be presented widely in both the academic and non-academic setting to make sure the findings have an impact. Funding applications will be developed based on the initial co-production of research questions and priority areas during network activities, working in partnership with policy makers and schools, and will sustain the network in the longer term.

THE PROBLEM There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than "health", which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from "health policy" to "healthy public policy". OUR POLICY FOCUS We will work with three policy partners at local, regional and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority and Scottish Government. We will focus on four jointly agreed policy priorities for good health: - Creating a fairer economy - Promoting mental wellbeing - Providing affordable, good quality housing - Preventing long-term effects of difficult childhoods. OUR COMPLEX SYSTEMS SCIENCE APPROACH Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g. housing) can have large unintended effects in others (e.g. poverty). There is often no "correct" solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes is crucial so failing policies can be revised or dropped. We propose to use complex systems modelling, which has been developed to understand and make projections of what might happen in complex systems given different plausible assumptions about future developments. Our models will be underpinned by the best available data and prior research in each policy area. Our new evidence about likely policy effects across a wide range of outcomes will help policy partners decide between alternative policies, depending on how important different outcomes are to them (e.g. improving health or economic growth). We will develop support tools that can visualise the forecasts, identify policies that achieve the desired balance between competing outcomes and update recommendations when new information emerges. Whilst new to public health policy, these methods are well-established in engineering and climate science. We will 1. Work with policy partners to understand the policy systems and evidence needs 2. Bring together existing data and evidence on each policy system (e.g. links between policies and outcomes, interdependencies between outcomes) 3. Explore citizens' preferences for prioritising when not all outcomes can be achieved 4. Link policies and their health and non-health effects in computer models to analyse benefits and costs over time 5. Build an interactive tool to help policy decision-making, inform advocacy action and support political debate. SIPHER's MAIN OUTCOME We will provide policymakers with a new methodology that allows them to estimate the health-related costs and benefits of policies that are implemented outside the health sector. This will be useful to our partners, and others, who want to assess how scarce public sector resources can be spent to maximise the health and wellbeing benefits from all their activities.