Modeling and predicting human behavior has been a lively research topic for decades. There is an entire established area, generally called behavior econometrics, that dedicates particularly to how people make choices, and how to use such knowledge to guide and inform interventional changes (e.g. policy making, infrastructure investments, marketing). Both from a research and practice stand-point, knowledge and data representation have followed the same principles for decades, essentially based on statistics and econometrics. However, such representations are arguably not ideal in many current challenges (e.g. social influence in behavior, hierarchical decision making, spatial and temporal causality).
In this presentation, I will show some recent work that tries to address some of these newly recognized challenges, using Machine Learning research, such as generative modeling, neural network embeddings, or context free grammars. My main goal is, however, to present and discuss the potential of a logic programming based direction, which I believe is very exciting.