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Abstract Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standarddual processtheories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule:no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings. 

Successfully navigating the social world requires reasoning about both high-level strategic goals, such as whether to cooperate or compete, as well as the low-level actions needed to achieve those goals. While previous work in experimental game theory has examined the former and work on multi-agent systems has examined the later, there has been little work investigating behavior in environments that require simultaneous planning and inference across both levels. We develop a hierarchical model of social agency that infers the intentions of other agents, strategically decides whether to cooperate or compete with them, and then executes either a cooperative or competitive planning program. Learning occurs across both high-level strategic decisions and low-level actions leading to the emergence of social norms. We test predictions of this model in multi-agent behavioral experiments using rich video-game like environments. By grounding strategic behavior in a formal model of planning, we develop abstract notions of both cooperation and competition and shed light on the computational nature of joint intentionality. 

Abstract Radiation belt electrons undergo frequent acceleration, transport, and loss processes under various physical mechanisms. One of the most prevalent mechanisms is radial diffusion, caused by the resonant interactions between energetic electrons and ULF waves in the Pc4‐5 band. An indication of this resonant interaction is believed to be the appearance of periodic flux oscillations. In this study, we report long‐lasting, drift‐periodic flux oscillations of relativistic and ultrarelativistic electrons with energies up to ∼7.7 MeV in the outer radiation belt, observed by the Van Allen Probes mission. During this March 2017 event, multi‐MeV electron flux oscillations at the electron drift frequency appeared coincidently with enhanced Pc5 ULF wave activity and lasted for over 10 h in the center of the outer belt. The amplitude of such flux oscillations is well correlated with the radial gradient of electron phase space density (PSD), with almost no oscillation observed near the PSD peak. The temporal evolution of the PSD radial profile also suggests the dominant role of radial diffusion in multi‐MeV electron dynamics during this event. By combining these observations, we conclude that these multi‐MeV electron flux oscillations are caused by the resonant interactions between electrons and broadband Pc5 ULF waves and are an indicator of the ongoing radial diffusion process during this event. They contain essential information of radial diffusion and have the potential to be further used to quantify the radial diffusion effects and aid in a better understanding of this prevailing mechanism.