Multilayer networks and games of incomplete information for infrastructure resilience

Infrastructure are socio-technical systems: they are comprised of (1) the technical, engineered side and (2) the social layer, that exchange stresses and strains leading at times to system failures. Resilience of such systems is demand-dependent. For example, the route and mode of transport to work determines the possibility of traffic congestions or station overcrowding. The peaks in the demand for electricity determine the likelihood of blackouts. These systems are often found to be driven towards a “tragedy of the commons” scenario, in which individually rational (but selfish) behaviours put at risk the infrastructure system integrity.

This PhD project investigates fundamental questions about how users take decisions based on the amount and type of information, to produce the onset of behaviours that boost performance and enhance resilience of spatially embedded infrastructure. A new understanding of the decision dynamics will be achieved using a multilayer network framework where (imperfect) information layers and a non spatially embedded social network can be superimposed to the physical infrastructure network.

The working hypothesis is that decisions are influenced by the amount of information available, the perception individuals have of their importance and of their reliability.

The project is proposed as a theoretical investigation but will use data for validation and benchmarking including Transport for London (TFL) passenger counts and Sheffield City Council (SCC) traffic data.

Understanding what information triggers virtuous decisions will not only preserve the health of the infrastructure but can be leveraged to meet targets such as reducing energy consumption and carbon emissions.

The PhD student will be based at the Department of Automatic Control and Systems Engineering, supervised by Dr Giuliano Punzo and be part of the Urban Flow Observatory to which objective they will be expected to contribute (https://urbanflows.ac.uk).

The successful candidate must start by October 2020.

Next steps 
Informal enquiries are welcome, please contact the lead supervisor Dr Giuliano Punzo, at [Email Address Removed] 

Applications should be made through the University of Sheffield and include a CV and a succinct one-page research proposal within this proposed topic area. Applications will be evaluated on a rolling basis. https://www.sheffield.ac.uk/acse/research-degrees/applyphd