In our digital and interconnected world we constantly leave digital traces logging our daily activities such as the movies we watch, the route we follow on our daily commute, the friendships we have in the online social network, and so on. Despite this wealth of data we still lack a solid theoretical ground and a comprehensive modelling framework to characterize the mechanisms shaping the evolution of the socio-technical systems surrounding us. My research work is to unveil and describe these underlying processes by using Complex Systems techniques in combination with machine learning tools. The results are interesting not only from a theoretical point of view but can also provide valuable decision support tools to understand, control and dynamically forecast the sustainability of such systems. I am also engaged in the development of digital platforms for sustainability to create playful environments aimed at assessing the present situation and allow citizens to participate in the decision process to conceive and simulate future scenarios.
The adverse effects of unsustainable behaviors on human society are leading to an increasingly urgent and critical need to change policies and practices worldwide. This requires that citizens become informed and engaged in participatory governance and measures leading to sustainable futures. Citizens’ understanding of the inherent complexity of sustainable systems is a necessary (though generally not sufficient) ingredient for them to understand controversial public policies and maintain the core principles of democratic societies. In this work, we present a novel, open-ended experiment where individuals had the opportunity to solve model urban sustainability problems in a purposeful game. Participants were challenged to interact with familiar LEGO blocks representing elements in a complex generative urban economic indicators model. Players seeks to find a specific urban configuration satisfying particular sustainability requirements. We show that, despite the intrinsic complexity and non-linearity of the problems, participants’ ability to make counter-intuitive actions helps them find suitable solutions. Moreover, we show that through successive iterations of the experiment, participants can overcome the difficulties linked to non-linearity and increase the probability of finding the correct solution to the problem. We contend that this kind of what-if platforms could have a crucial role in future approaches to sustainable developments goals.