Modelling of reputational dynamics - Applications with telematic environments

Information and Communication Technologies (ICT) can facilitate the achievement of both EU Societal Challenges objectives and UN Sustainable Development Goals. However, to fully exploit ICT's potential and thus effectively solve global-scale problems, it is fundamental to understand how human beings relate to ICT and which dynamics exist within the environments made available by new technologies (e.g., virtual environments). The present work is structured as follows. The first part is focused on collective problem-solving and decision-making within ICT. Numerical simulations (i.e., agent-based modeling) have been performed to highlight which interaction/environmental characteristics (such as group size, task complexity, the ratio of cooperators within the group) are important to reaching large scale cooperation and thus overcome well-known psychological phenomena that usually hinder human cooperation (e.g., free-riding, Ringelmann effect, sucker effect). The second part concerns reputation effects in virtual environments. Indeed, reputation has been often used in web-based systems to enhance people's cooperation levels. Several empirical experiments based on game theory (e.g., ultimatum game) have been carried out online. Through these experiments, it was possible to estimate how much people rely on reputation in their decision making, how much their fairness is affected by reputational score, and whether online reputation can induce biases. Moreover, how reputation is built in online interactions (i.e., which factors contribute to define an individual's reputation) has been investigated. Finally, in the third part, a particular human-ICT interaction behavior (i.e., phubbing) has been studied. Phubbing has been often defined in terms of psychopathology. However, phubbing can be reasonably linked also to the increased availability of virtual social environments. Data were collected through online questionnaires and surveys and a multidimensional model for phubbing was produced. On the one hand, the obtained model clarify which are the phubbing's antecedents and thus make possible to assess users’ phubbing potential risk. On the other, the model provides useful insights for dedicated mobile device settings to help reduce ICTs’ pervasivity for potential phubbers.