Let’s imagine an environmental planning and policy design scenario, where multiple stakeholders’ input (from government, society, NGOs, environmental experts, corporations, etc.) is aggregated in order to make decisions on allocation of financial resources towards the conservation of specific species and ecosystems. How to capture the opinion of all these stakeholders (who have very different interests)? And how to account for uncertainty in the information they provide (i.e. opinions are affected by the context, experience and memory of each of the stakeholders participating)?
Environmental Planning and Policy Design is in fact a very challenging matter. Different stakeholders are involved, there are limited financial and HR resources, data is complex, governmental regulation has to be complied with and evaluation is hard to undertake. However, with the recent development of computational intelligence tools such as fuzzy sets it seems that some of the challenges mentioned can be reduced or sometimes eliminated.
Last Thursday, in the first event of the SRN hosted in Jubilee Campus, Dr Christian Wagner, a transitional fellow in Computer Science and the Horizon Digital Economy Research Institute, immersed us in the journey of the ongoing research collaboration between the University of Nottingham and the Western Australian Government’s Department of Parks and Wildlife. The afternoon talk gathered early career researchers from the schools of Computer Science, Physics, Law and the Doctoral Training Centre (DTC) who raised interesting questions regarding the process of framing the values driving the planning approach for natural biota and the actors behind the development the value classification.
As Dr Christian Wagner mentioned in his talk, fuzzy sets have been used in areas such as crowdsourcing, but the objective now is to make the tool applicable in research areas in which the capture, modelling and interpretation of uncertainty in information is a challenge. This will be achieved, as he highlighted, by the use of a cloud-based system through which other researchers can upload their own data and perform their own analysis.
Dr Christian Wagner completed his PhD in Computer Science at the University of Essex, UK in 2009. His research is centred on the development and application of novel computational intelligence tools, with a focus on the capture, modelling and interpretation of uncertainty in information in areas including robotics, intelligent building and smart agents. Most recently, he has focused on the capture and aggregation of heterogeneous information sources including both quantitative sources (e.g., sensors) and qualitative sources (e.g., people).