Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent a...

Argumentative discussion is a highly demanding task. In order to help people in such discussions, this article provides an innovative methodology for developing agents that can support people in argumentative discussions by proposing possible arguments. By gathering and analyzing human argumentative behavior from more than 1000 human study participants, we show that the prediction of human argumentative behavior using Machine Learning (ML) is possible and useful in designing argument provision agents. This paper first demonstrates that ML techniques can achieve up to 76% accuracy when predicting people’s top three argument choices given a partial discussion. We further show that well-established Argumentation Theory is not a good predictor of people’s choice of arguments. Then, we present 9 argument provision agents, which we empirically evaluate using hundreds of human study participants. We show that the Predictive and Relevance-Based Heuristic agent (PRH), which uses ML prediction with a heuristic that estimates the relevance of possible arguments to the current state of the discussion, results in significantly higher levels of satisfaction among study participants compared with the other evaluated agents. These other agents propose arguments based on Argumentation Theory; propose predicted arguments without the heuristics or with only the heuristics; or use Transfer Learning methods. Our findings also show that people use the PRH agents proposed arguments significantly more often than those proposed by the other agents.

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.