Details of Award
NERC Reference : NE/N000595/1
Predictive models and programming skills using agent-based models
Training Grant Award
- Lead Supervisor:
- Professor R Stafford, Bournemouth University, Sch of Applied Sciences
- Grant held at:
- Bournemouth University, Sch of Applied Sciences
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Freshwater
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Complexity Science
- Agent-Based Models
- Uncertainty in complex systems
- Behavioural Ecology
- Conservation Ecology
- Population Ecology
- Abstract:
- The training will introduce participants to a type of predictive modelling (agent-based modelling), which is growing in popularity and use in the academic community (in environmental research, but also in many other areas). Agent-based modelling is especially intuitive to many environmental scientists, as in its simplest form it can be thought of as modelling the behaviour of an animal (the agent) to its local, perceived environment (things the animal can see, hear, smell etc), although applications go well beyond models of animal behaviour. Predictions from agent-based models can be powerful, and have been applied (by staff involved in the training) to topics as diverse as wading bird energy budgets, fisheries research and evolutionary biology. Some of this work has gone on to inform national policy on threats of disturbance to bird populations and river management for fish. Due to the intuitiveness of agent-based models, they are an ideal method of getting to grips with scientific programming. As such, the course will not only provide participants with an ability to create and use agent-based models, but also with the ability to understand scientific programming, especially with regard to control structures such as loops and decision making processes. We will also include training in novel techniques, such as Approximate Bayesian Computing, which is highly useful in finding the necessary numeric parameters for the models, and has been recently developed as part of a NERC research grant. The course is unique in teaching a wide range of methods (developed by two different research groups involved in ABMs) in a hands on, practical manner, but also in providing training and advice based on experience of using these models to inform decision making beyond academia. The impact of the research will be that a significant number of students or early career researchers are trained in these intuitive and in-demand methods. They will not only learn how to create models, but how to evaluate their models, and those of others, and apply them to real world problems. The greater establishment of these techniques in the scientific community will also allow for a greater understanding of their value by policy makers, and ultimately will contribute to greater acceptance of new modelling approaches in the wider environmental community.
- NERC Reference:
- NE/N000595/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £17,775
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £17,775 |
If you need further help, please read the user guide.