Many social, political and economic structures can be understood through bottom up computational methodologies such as agent based modelling for decomposing these systems into their various actors and components, modelling their characteristics, and interaction behaviours. These models need to be based on real-world data sources which are fraught with uncertainties pertaining to noise, human decisions, knowledge perception, reliability, trust and levels of agreement between stakeholders. These sources of uncertainties can be managed and handled using fuzzy and probabilistic representation, aggregation and reasoning methodologies.
New developments in top down CI algorithms such deep learning approaches can be used to model real world and simulated processes to emulate complex patterns and correlations in dynamic and historical data that can also be used for empirical validation and estimation.
Advances in evolutionary algorithms can be developed for assessing and optimizing policies and strategies in terms of simulating their impact on aspects such as labour and employability modelling complex negotiation processes and improving the syntactic, semantic and search capabilities of evolutionary algorithms for handling multifaceted real-world problems.
These computational techniques can be applied to understand population mobility, economic growth, social behaviour, human sentiment, wellbeing, security and political risk, education, welfare, geopolitics and environmental concerns.