Michael Lewis

In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective and interpretable interventions. Our method leverages DRL to provide expert action recommendations while incorporating ToM modeling to understand users’ mental states and predict their future actions, enabling appropriate timing for intervention. To explain interventions, we use counterfactual explanations based on RL’s feature importance and users’ ToM model structure. Our proposed system generates accurate and personalized interventions that are easily interpretable by end-users. We demonstrate the effectiveness of our approach through a series of crowd-sourcing experiments in a simulated team decision-making task, where our system outperforms control baselines in terms of task performance. Our proposed approach is agnostic to task environment and RL model structure, therefore has the potential to be generalized to a wide range of applications.

Italian philosophy constitutes one of the most vibrant and fruitful areas in contemporary thought, bringing extraordinary novelty to some of the oldest tropes, from human nature to the relation between political power and life, the thinking of actuality and potential, and the nature of work and labour. This reader includes texts by the most renowned thinkers, from Dante and Machiavelli to Giorgio Agamben, Antonio Negri, and Roberto Esposito, all of which are introduced by an expert on the particular thinker, and situated within the context of their work as a whole. The Bloomsbury Italian Philosophy Reader provides a unique resource for students and scholars alike, covering the history of Italian thought to the present day.