Infrastructure systems play a crucial role in delivering social and economic wellbeing but radical transformation is required to ensure their sustainability and resilience to social and environmental change. This is particularly important in cities where infrastructure is most dense and interdependencies between infrastructures, economies and society are most profound. However, taking decisions to transform urban infrastructure is extremely difficult because it is a highly complex socio-technical system; there are many, profound uncertainties in both how the physical infrastructure will change but also in how people make decisions; and there isn’t one organisations responsible for its transformation.

Because of this we need an approach which incorporates decisions taken by multiple actors, at multiple levels and in different infrastructure systems; addressing the links between decision makers, not just economic and technical links between infrastructure systems. A number of approaches exist, which aim to help decision makers to understand and manage deep uncertainties associated with long-term decisions in complex systems, which go beyond classical risk assessment. However, most approaches and supporting models assume that there is one decision maker with clearly defined objectives and stable preferences. The need for co-ordinated action from decision makers with differing motivations and agency is an additional uncertainty that is rarely recognised.

This project will develop:

  • methods to capture decision maker interactions and how these influence infrastructure transformation;
  • models that combine decision maker interactions with infrastructure system interactions; and
  • an approach that draws methods and models together to develop adaptive and dynamic pathways for infrastructure transformation.

In combination these methods create a multi-actor, adaptive decision making (MAADM) approach to support the development of long-term transformation strategies. This will allow local authorities, policy and business actors to develop adaptive, evidence-based strategies based on an understanding of key interdependencies and uncertainties.