The predominant activity in risk assessment is the modelling of physical hazards. Yet recent major risk events, such as the Sudan 1 food contamination scandal, show how important the social response can be in comparison to physical harm. Withdrawals of product, loss of reputation, reductions in trust, additional testing and inspection regimes, and so on can often be just as consequential as physical injury. Our main basis for understanding the social response to risk events is the Social Amplification of Risk Framework due to Kasperson et al (1988). But this remains a qualitative model, and is accepted even by its authors (for example Kasperson et al 2003) as a 'framework' for organising our general understanding rather than a theory that will predict or explain the social construction of risk in a definite way.Our aim is to determine whether, and investigate how, we can make the concepts which appear in social risk amplification models more precise and more quantitative. To do this we propose to explore a variety of techniques used in the discipline of epidemiology - on the basis of a number of apparent parallels. For example, notions of susceptibility and infection seem to be analogous to notions of sensitivity and concern, 'super-infectives' resemble certain social institutions such as the broadcast media, and the recrudescence of infection resembles the recrudescence of concern and 'ripple effects' found in risk amplification. The programme will involve applying a number of techniques to the various kinds of data we have about social response (for example the uptake of vaccinations), in the context of several recent case studies. We plan to assess how informative these are in the decision processes of our collaborator - the Department for Environment Food and Rural Affairs.

The predominant activity in risk assessment is the modelling of physical hazards. Yet recent major risk events, such as the Sudan 1 food contamination scandal, show how important the social response can be in comparison to physical harm. Withdrawals of product, loss of reputation, reductions in trust, additional testing and inspection regimes, and so on can often be just as consequential as physical injury. Our main basis for understanding the social response to risk events is the Social Amplification of Risk Framework due to Kasperson et al (1988). But this remains a qualitative model, and is accepted even by its authors (for example Kasperson et al 2003) as a 'framework' for organising our general understanding rather than a theory that will predict or explain the social construction of risk in a definite way.Our aim is to determine whether, and investigate how, we can make the concepts which appear in social risk amplification models more precise and more quantitative. To do this we propose to explore a variety of techniques used in the discipline of epidemiology - on the basis of a number of apparent parallels. For example, notions of susceptibility and infection seem to be analogous to notions of sensitivity and concern, 'super-infectives' resemble certain social institutions such as the broadcast media, and the recrudescence of infection resembles the recrudescence of concern and 'ripple effects' found in risk amplification. The programme will involve applying a number of techniques to the various kinds of data we have about social response (for example the uptake of vaccinations), in the context of several recent case studies. We plan to assess how informative these are in the decision processes of our collaborator - the Department for Environment Food and Rural Affairs.

In this proposal we will investigate if we can simulate regulatory group decision making utilising an environment of cooperating and automated decision makers. This ambitious project comprises a multi-disciplinary, inter-institutional team drawn from Computer Science, Psychology and Industrial and Manufacturing Science. The four investigators will be supported by a three year PhD student, based at Cranfield, and a two year Nottingham based research assistant.The first phase of the project will define the regulatory decision contexts (environmental planning, permitting, policy development), individual roles and personality influences. These factors will be incorporated in the simulation environment in the second phase of the project and the resulting system will be analysed in the final phase.The second phase of the project will take the outputs from phase one and create a simulation environment. This environment will initially be built using agent based technology, but other machine learning approaches will be investigated as appropriate.The final phase will involve all members of the team in analysing the resulting system and calibrating its parameters in order to emulate group decision making processes as closely as possible.A key feature of this proposal are 2-day workshops at nine monthly intervals in order for the whole team (investigators, PhD student, RA and project partners) to work together in a focused way.

Meso-Net is a Network project over three years which brings together the science and user communities involved in air quality research and assessment. Mathematical models are a key tool for air quality research and policy support. Over the past few decades most models for air quality applications have been based on simpler approaches. Although these models require modest input data and computer performance, they include significantly simplified treatment of emissions, meteorology and atmospheric chemistry. However, a number of numerical models now exist that include a more complete treatment of atmospheric dynamics and chemistry for air quality applications on urban to regional scales. Although much effort has been devoted in this area internationally, UK on the whole has been slow to benefit from new modelling developments. Recently, research groups and users within the UK have started to adapt and use such models for air pollution research and assessment. Furthermore, initiatives are underway to further develop the Met Office's Unified Model for air quality and climate applications on a range of spatial scales. The overall aim of Meso-Net is to significantly improve the UK capability for developing and applying high resolution air quality mesoscale models to address the needs of policy, regulation and research. From the user perspective there is a need for integrated policies to reduce the impact of air pollution on different spatial and temporal scales. Meso-Net will encourage consistent and coherent approaches and will provide a framework to stimulate such work as well as carry out specific activities mentioned below. Meso-Net will establish robust communication and interaction mechanisms between the air quality, meteorological and climate research communities and policy, regulation and industrial users. It will strengthen knowledge transfer from the science community to users to provide new generation models to support policy and regulation needs. It will do this by establishing direct interaction between the science and user communities through workshops, working groups, seminars, exchange visits and training sessions. It will lead to practical frameworks for users and the research community to have access to the latest advanced models for solving air quality problems. Meso-Net will have a major impact in the following ways: (i) It will provide, for the first time, strategic coordination of a wide range of disparate activities in the UK on mesoscale modelling for air quality applications and a mechanism for users to derive maximum benefit from research developments in this area; (ii) It will coordinate the current support structures to address the needs of the growing advanced air quality modelling community; (iii) It will stimulate interaction between the different sections of the atmospheric science communities which hitherto have tended to work separately with separate goals; (iv) It will significantly enhance the international competitiveness of UK capabilities in high resolution modelling for air quality research and policy applications.