Modeling Decision-making in Social Systems: a Network Dynamical Systems Approach

The aim of this work is to advance our ability to model the decision making-process in social systems, with the overarching goal of helping policymakers and government bodies take mathematically backed decisions. Toward this aim, we adapted a complex network approach, leveraging tools from network science, control theory, and social science which altogether offers a powerful and multidisciplinary approach to tackle the complexity that pervades social dynamics. We explored the conditions under which we can confer given controllability and observability properties to complex networks of dynamical systems, so to have an insight on our ability to steer or monitor their collective behaviors in the presence of realistic constraints. Then, we studied the opinion dynamics in groups of interconnected individuals discussing on a given topic, that is, we analyzed the evolution over time of their opinions under the effect of social ties and other psychological traits, such as stubbornness or the tendency to conform. Opinions dynamics models enable us to describe the collective behaviors that occur in real world social groups and to unveil how the social interactions, namely the peer pressure and other biases, shape our opinion formation and result in the group exhibiting behaviors such as consensus, disagreement, or polarization of opinions. In particular, we analyzed how external influences, such as the ones exerted by opinion leaders (the so-called influencers) can steer the opinion profile of a social group towards a desired state at steady state. To this aim, we borrowed a tool from network control, namely pinning control, to show how agents with relatively few connections can exploit the structure of the social interconnections to diffuse their influence throughout the social group. Using heuristic approaches and leveraging theoretical and graphical knowledge of the network dynamical systems under investigation, we showed that a smart selection of the individuals to directly influence allows to maximize the effect of persuading actions of opinion leaders. Finally, we illustrated how the model we proposed can provide quantitative predictions on opinions' distribution in a given population, which in turn can be used to gauge the effectiveness of different awareness campaigns strategies aimed at mitigating vaccine hesitancy.