Causal reasoning and decision-making processes

Abstract

Collective phenomena in the real world often occur in systems that have multiple types of individuals, each one with its behavior and decision-making. Such a feature can be captured with the use of information theory and causal inference tools, which have been intensely used to quantify individuals' influence. We aim at enhancing the understanding of causal reasoning in decision-making processes by two research proposals. First, the study of dependent causes in the time-continuous case, once it is still not well understood due to the lack of a theoretical formalism. To overcome it, we propose to apply the candidate's techniques developed in his Ph.D. research. Second, a social media scenario analysis will be performed. Previous works show that a group's influence is not just the simple addition of individuals' influence, such observation combined with correct causal quantifiers may explain interesting emergent phenomena. This research will be performed using twitter data. The academic gains will be important for the candidate's Ph.D. project as well as a better comprehension of the collective behavior of populations from the causal reasoning perspective.