Data driven agent-based micro-simulation in social complex systems

We are recently witnessing an increase in large-scale micro/individual/- granular level behavioural data. Such data has been proven to have the capacity to aid the development of more accurate simulations that will ef- fectively predict the behaviours of complex systems. Despite this increase, the literature has failed to produce a structured modelling approach that will effectively take advantage of such granular data, in modelling com- plex systems that involve social phenomenons (i.e. social complex sys- tems). In this thesis, we intend to bridge this gap by answering the question of how novel structural frameworks, that systematically guides the use of micro-level behaviour and attribute data, directly extracted from the ba- sic entities within a social complex system can be created. These frame- works should involve the systematic processes of using such data to di- rectly model agent attributes, and to create agent behaviour rules, that will directly represent the unique micro entities from which the data was ex- tracted. The objective of the thesis is to define generic frameworks, that would create agent based micro simulations that would directly reflect the target complex system, so that alternative scenarios, that cannot be inves- tigated in the real system, and social policies that need to be investigated before being applied on the social system can be explored. In answering this question, we take advantage of the pros of other model- ing techniques such as micro simulation and agent based techniques in cre- ating models that have a micro-macro link, such that the micro behaviour that causes the macro emergence at the simulation’s global level can be easily investigated. which is a huge advantage in policy testing. We also utilized machine learning in the creation of behavioural rules.This created agent behaviours that were empirically defined. Therefore, this thesis also answers the question of how such structural framework will empirically create agent behaviour rules through machine learning algorithms. In this thesis we proposed two novel frameworks for the creation of more accurate simulations. The concepts within these frameworks were proved using case studies, in which these case studies where from different so- cial complex systems, so as to prove the generic nature of the proposed frameworks. In concluding of this thesis, it was obvious that the questions posed in the first chapter had been answered. The generic frameworks had been created, which bridged the existing gap in the creation of accurate mod- els from the presently available granular attribute and behavioral data, al- lowing the simulations created from these models accurately reflect their target social complex systems from which the data was extracted from