Core aim

Understanding, predicting and controlling large-scale human interactions with natural systems is one of the great challenges of our time – in essence, how we continue to use natural services without damaging the underlying infrastructure. Understanding the dynamics of coupled human-environmental systems, and their response to given interventions, is a hard problem and the outcomes are often counter-intuitive. Our aim is to improve this situation through the creation, provision and use of novel analytical and computational tools.

'Complex human-environmental systems'

‘Complex’ in this context refers to systems made up of many interacting parts, in which the ‘emergent’ behaviour of the system can be qualitatively different to the behaviour of the constituent elements. These systems are characterised by heterogeneity and emergence on many interacting scales, an ability to adapt as conditions change, and by being difficult to predict. They are common, with examples in many physical, biological, technological and social domains. Concrete examples include ecosystems, social networks, economic markets. Complex systems have been well-studied, particularly over the past 20-30 years, both empirically and with theoretical tools such as network/graph theory, agent-based and cellular automata modelling. You can find some background here.

‘Human-environmental systems’ is another broad term, referring to any system that has a strong human presence (e.g. social or economic systems), a ‘natural’/environmental component (e.g. ecological or geomorphological systems) and significant connections/feedbacks between them (e.g. use of natural resources).

Methods and focus

We are interested in understanding the dynamics of human, environmental, and coupled human-environmental systems, and use a variety of mathematical modelling, numerical simulation, empirical data analysis, and experimental methods. Our environmental focus is typically on the dynamics of ecosystems, or the parts of ecosystems relevant to service provision, and their response to human impacts. Some models are non-spatial coupled ODE models, others are data-driven (e.g. artificial neural network models), and some spatially explicit. To represent human behaviour we model the underlying psychology of individual-level decision-making within uncertain changing environments, and how this leads to the emergence of population-level effects in larger social networks. 

Humans are represented in some models as explicit autonomous individual agents (agent-based models), while in others they are treated in aggregate.  In our more applied projects we aim to answer specific questions about the sustainability of human-environmental interactions (resource use, conservation, agriculture), mostly, but not exclusively, in relation to marine systems. We collaborate broadly across multiple sectors, combine our models with management decision-making processes, inform policy-makers, and attempt to gain insights in to interventions on different spatial and temporal scales.

© 2021 CoHESyS-Lab