Evolution and resilience of industrial ecosystems
Start date01 March 2010
End date29 February 2016
The Evolution and Resilience of Industrial Ecosystems programme (ERIE) will address a series of fundamental questions relating to the application of complexity science to social and economic systems. Our programme of research aims to embed cutting-edge complexity science methods and techniques within prototype computational tools that will provide policymakers with realistic and reliable platforms for strategy-testing in real-world socio-economic systems.
Aims and objectives
The programme includes the gathering of data from case studies, the development and application of appropriate theoretical and computational techniques, simulation using agent-based models and the incorporation of all these elements into 'serious games' for use by policymakers. We will study the negotiation of policy goals and options, explore the role of models in policymaking and involve policymakers in the design and testing of our strategy tools.
The programme will focus on a crucial aspect of the UK economy: the way in which firms are interdependent on each other, with the interrelationships being multi-level and multi-valued. Within an industrial 'ecosystem', there can be relationships of supply and demand; the transfer of knowledge; competition for labour; the transfer of materials down supply chains; negotiation over standards; collaboration in trade associations and unions; and innovation, product differentiation and branding.
We will use mathematical and computational approaches to model these layered, nested, multiscale systems, where the links between actors are dynamic and the exchanges between them are unpredictable, fluctuating and perhaps sporadic. Within this context we will examine concepts and measures of resilience (the ability to recover from external shocks), emergence (the ways in which social institutions arise from individual activities) and immergence (the ways in which individuals react to institutional constraints). This leads us to some of the most intriguing open questions of complexity science. We will seek answers inspired by the real-world industrial ecosystems as captured in our case studies.
Our vision is to provide models of multi-level socio-economic systems that are useful for decision-makers aiming to 'steer' towards policy-relevant goals. It is not our intention to provide 'the' policy solution to policy problems (specifically, it is not our intention just to show how particular ecosystems may be made more resilient or more sustainable), but rather to provide a suite of tools which will allow decision makers and their representatives to investigate alternative scenarios given a set of assumptions and initial conditions.
We will apply the methods of data assimilation, largely developed in the context of weather forecasting, to incorporate the inevitably incomplete data from case studies into agent-based models, on an ongoing basis, with the aim of providing 'predictive' tools that are continually updated with real-world data. By 'prediction' here we mean the identification of alternative scenarios along with estimates of the probability that each will be realised over given time frames, and estimates of the sensitivity of these to uncertainties in the data and underlying model.
It is an integral part of ERIE to study - and involve - those involved in the case study sites. One research stream is concerned with studying those with a stake in the system, as controllers, decision makers, customers, workers, etc., their goals, policy options and their links with the industrial ecosystems that they are interacting with.
The research programme is divided into four streams, each consisting of a number of cross-disciplinary projects. Four post-doctoral researchers and a project officer will work on the programme, with seven Investigators from the disciplines of mathematics, computing science, environmental science and sociology, and 9 PhD research students, the latter funded from internal University of Surrey resources.