Reforming the Foreign Tax Credit, Subpart F, and GILTI in Light of Pillar Two

The foreign tax credit has been a cornerstone of the United States international tax system since as early as 1919. However, there have been a number of recent developments in the international tax landscape that warrant a significant revision to the foreign tax credit rules as they apply to controlled foreign corporations (“CFCs”). In particular, the current global tax deal known as the OECD/G20 Inclusive Framework, Pillars One and Two, may cause the United States to lose significant tax revenue if changes are not made to international tax rules, including the foreign tax credit. Thus far, the United States has shown little political will to implement either Pillars One or Two, but dozens of countries have passed legislation implementing the Pillar Two global minimum tax as early as 2024 or 2025 that will impact U.S. multinationals, and many more are poised to do so. Additionally, changes to the foreign tax credit rules, along with changes in the taxation of the foreign income of CFCs as part of the 2017 Tax Cuts and Jobs Act (“TCJA”), as well as new foreign tax credit Regulations (the “2022 Regulations”), have reopened a number of debates and controversies about the operation of the foreign tax credit. These pressure points indicate it may be time for more comprehensive reform of the U.S. foreign tax credit rules. At a basic level, the current regime allows a credit, up to the U.S. income tax rate, for foreign taxes that are income taxes and that are imposed on foreign source income. One of the main issues raised in the controversy of the 2022 Regulations is the definition of an income tax. To avoid such definitional problems, as well as planning opportunities presented by the current rules, reform proponents have identified several possible options for reform. Among these options are: “grading,” or allowing partial credits for different types of taxes; “leveling down,” namely eliminating the foreign tax credit and permitting deductions for all foreign taxes of every type (income and non-income) as costs of doing business; “leveling up,” making all foreign taxes of every type creditable, even non-income taxes; and “deconstructing,” or taking apart each tax into income and non-income parts and crediting only the income tax part. Building on this prior work, this paper argues for a modified leveling up approach. Given that the international community as a whole is set to adopt new taxes of questionable creditability, U.S.-based multinational entities (“MNEs”) would be left at a significant disadvantage were the U.S. to completely deny creditability to these new taxes. Specifically, this paper argues that the U.S. can maintain the current rate of corporate taxation at 21%, with no deferral, by allowing a deemed paid credit of 15% of worldwide income for any tax paid by a domestic corporation or CFC. The domestic corporation or CFC would not have to demonstrate that the tax was an income tax. For taxes in excess of the 15% rate, the income tax status of the tax would have to be proven. However, a broader definition of income tax would be adopted similar to the definition prior to the adoption of the recent regulations and incorporating certain new taxes under Pillars One and Two

Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel ϵ-greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that ϵ-greedy algorithms are generally at least as effective as conventional acquisition functions (e.g. EI and UCB), particularly with a limited budget. In higher dimensions ϵ-greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution

Scientists’ warning on affluence

For over half a century, worldwide growth in affluence has continuously increased resource use and pollutant emissions far more rapidly than these have been reduced through better technology. The affluent citizens of the world are responsible for most environmental impacts and are central to any future prospect of retreating to safer environmental conditions. We summarise the evidence and present possible solution approaches. Any transition towards sustainability can only be effective if far-reaching lifestyle changes complement technological advancements. However, existing societies, economies and cultures incite consumption expansion and the structural imperative for growth in competitive market economies inhibits necessary societal change

Towards Learning of Safety Knowledge from Human Demonstrations

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, a new kind of hazards being implicated by the robot’s capability to manipulate the environment occurs: hazardous environmental object interactions. One of the open questions in safety research is integrating safety knowledge into robotic systems, enabling these systems behaving safety-conscious in hazardous situations. In this paper a safety procedure is described, in which learning of safety knowledge from human demonstration is considered. Within the procedure, a task is demonstrated to the robot, which observes object-to-object relations and labels situational data as commanded by the human. Based on this data, several supervised learning techniques are evaluated used for finally extracting safety knowledge. Results indicate that Decision Trees allow interesting opportunities

Robust Exploration/Exploitation Trade-Offs in Safety-Critical Applications

With regard to future service robots, unsafe exceptional circumstances can occur in complex systems that are hardly to foresee. In this paper, the assumption of having no knowledge about the environment is investigated using reinforcement learning as an option for learning behavior by trial-and-error. In such a scenario, action-selection decisions are made based on future reward predictions for minimizing costs in reaching a goal. It is shown that the selection of safetycritical actions leading to highly negative costs from the environment is directly related to the exploration/exploitation dilemma in temporal-di erence learning. For this, several exploration policies are investigated with regard to worst- and best-case performance in a dynamic environment. Our results show that in contrast to established exploration policies like epsilon-Greedy and Softmax, the recently proposed VDBE-Softmax policy seems to be more appropriate for such applications due to its robustness of the exploration parameter for unexpected situations

Conceptual Design of a Dynamic Risk-Assessment Server for Autonomous Robots

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, there is a new kind of hazards being implicated by the robot’s capability to manipulate the environment: Hazardous environmental object interactions. Therefore, the realization of the Dynamic Risk-Assessment approach with special scope on object-interaction risks is addressed in this paper. A server-based architecture is proposed facilitating a feasible integration into robotic systems and realization of software and hardware redundancy as well