Data-driven modelling of human behaviour with complex networks

Evolving environments and a growing number of sources for data offer new and interesting possibilities for studying the behaviour of individuals, groups and populations. This data from mobile phones, websites and social media provides opportunities for creating data-driven models where the occurring events, such as pandemics, can be considered as natural experiments in the given system altering the human behaviour therein. In addition to observational data, conducting controlled game experiments with agents and humans can be used for studying micro-level actions and decisions in order to understand the behavioural aspects relevant to emerging sociotechnical systems. Data-driven modelling typically focuses on prediction and explanation of the studied phenomena. Where models with high complexity have been shown to excel in prediction accuracy, interpretable and explainable models are appropriate for studying the complex human behaviour. This doctoral thesis presents data-driven modelling paradigms in studying human behaviour in cooperative games, mobility and cyber space using complex networks. The four research articles focus on interpreting human behaviour and decision-making in the sets of data through the modelling frameworks. The first two publications study the human decision making in a cooperative game with non-overlapping information and the effects from the presence of autonomous agents by conducting two game experiments. First we present a computational model based on probability matching and show that the human perception of risk during the experiment was near optimal while the rationality of choices was not. In the second publication the model is used for agents in a human-agent hybrid experiment. The group composition of humans and agents was shown to affect the game performance and the adaptation to the strategies of the agents with different game objective. The third publication studies human mobility during the COVID-19 pandemic in Finland using aggregated data from mobile phones. We consider the activity data as a set of bipartite networks and investigate projected exposure networks between postal code areas. The projected networks are modelled using gravity and radiation models with population data over the years 2019--2021 and the changes in the networks and model coefficients are analyzed in relation to the pandemic and the related effects of non-pharmaceutical interventions. The model parameters are shown to remain stable before the pandemic and once the pandemic begins they show a correlation to indices of intervention stringency. The final article of this dissertation presents a novel framework for constructing knowledge graphs from unstructured reports of cyber-attacks to create a systemic model for visual analysis for domain experts and for estimating risk in the network of entities connected by their high-level relationships and attributes. We implement the framework pipeline and evaluate the risk measure using a collected set of news reports.