Socio-economic Systems / Complex Systems
Network science is a relatively young academic subject that investigates complex systems represented as networks or graphs, with nodes representing separate entities and links or edges representing relationships between them. Graph theory from mathematics, social structure from sociology, statistical mechanics from physics, data mining and information visualisation from computer science, and inferential modelling from statistics are all used in this discipline. Network science is generally concerned with deducing prediction models for social, economic, physical, and biological events or systems.
One of this research group’s primary research interests is in the developing topic of Computational Social Science, which aims to improve the operation and performance of socio-economic systems and their entities by analysing influential aspects such as interdependencies between entities (which can be modelled based on networks). To do this, we look at system uncertainties and probable disruptions in entity function and/or interdependency, as well as their cascade effects. This information is subsequently used to design tools and methodologies, such as intelligent decision support systems, to improve the performance of socio-economic systems while taking into account the trade-offs between uncertainty and minimising the consequences of disruptions, such as rapid shifts. Assessing how interactions and collaborations among people or organisations in their social or professional networks, or suppliers and distributers in supply chain networks, can improve performance or outcome in terms of individual accomplishments (e.g., higher productivity, better services) or organisational goals (e.g., higher profits, successful completion of projects, faster and better response to emergency calls, and resilience against disasters and viral disease) are examples of this. This relatively young field combines 'network and computer sciences' (social network analysis and machine learning-based predictive modelling) with ‘management and decision sciences' (optimisation and multi-criteria decision making).
Impact
Our research is aimed at delivering:
- Efficient behavioural decision making by understanding the human social behaviours in complex supply chain networks or business models.
- Integrated measures to better reflect the strategic position of nodes in networks to develop theoretical-based measures that consider a diverse range of structural attributes of nodes in networks
- Efficient approaches to detect and predict events (e.g., innovation, topics, riots) from big data and social media
- More effective uncertainty management
- Improved utilisation of optimisation and multi-criteria decision-making (MCDM) approaches
Competitive advantage
Our research group bring together experts with remarkable track records in Cross-disciplinary research areas (such as supply chain management, network science, evolutionary algorithms, artificial intelligence business modelling, and simulation & modelling). We have long-standing experience in working with different project scheduling problems and then, consecutively, solving them by different advanced evolutionary algorithms. We can help organisations to better understand their capacity system and how it may lead to enhanced outputs and results, starting with the assumption that the focus should be on achieving high performance. Our emphasis on collaborating with organisations and doing applied research helps practitioners get the information and skills they need to put our research results into practice. The research team has extensive experience designing and implementing funded research from the ARC, industry partners, and other sources. We ensure that for any potential project design, the composition of the research team and the budget required for that project will reflect a concerted effort to calibrate a project that accounts for common project risks. In addition, we endeavour to communicate our findings to the public using local and national online news and communication platforms such as LinkedIn and other social media. Through UNSW, this research group has access to the supercomputer at National Computational Infrastructure (NCI), which is used to develop and test the developed algorithms. We have the necessary office space, access to information and library resources necessary to carry out any possible research matters.
