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"... this paper considers what problem, if any, the risk-based approach seeks to solve. It suggests that the problem to be solved by the approach is not primarily how to manage AI risks, but how to avoid a potentially over-broad scope of the regulation—a potential created by the broad definition of AI included in the Proposal."
" The introduced valuation principle relies on stochastic ordering so that the valuation risk-loading, and thus risk premiums, generated by the measure distortion is an ordered parametric family. The quantile processes are generated by a composite map consisting of a distribution and a quantile function."
"... this paper introduces a generic simplified solution for plantwide auxiliary diagnosis through Bayesian inference and quantification results from a probabilistic risk assessment (PRA) report, which is cost-efficient to implement and helpful in decision making."
"This paper... reviews the different channels of transmission of prudential policy highlighted in the literature and... provides a quantitative assessment of the impact of Basel III reforms using "off-the-shelf" DSGE models. It shows that the effects of regulation are positive on GDP whenever the costs and benefits of regulation are both introduced."
"Financial regulation will be sustainable for long horizons and uncertain risks if it removes the principle of continuity from its probabilistic background."
"This paper studies the design of Pareto-optimal reinsurance contracts in a market where the insurer and reinsurer maximize their expected utilities of end-of-period wealth. In addition, we assume that the insurer and reinsurer wish to control their solvency risks, which are defined through distortion risk measures of their end-of-period risk exposures."
"... our results imply that when derivatives-related proprietary costs are high, benefits of non-compliance likely outweigh the costs."
"The contribution discusses the current AI Act as proposed in April 2021, thereby focusing on two particular areas: EU non- discrimination law and EU law on occupational health and safety (OSH), as these two areas are, more or less explicitly, addressed as legal fields in the AI Act."
"We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control."
"The model is a comprehensive template for assessing loss and subsequently the insurance for activities in the Arctic and sub-Arctic regions. Governmental and non-government organisations alike will benefit from the tool by using it as a loss estimation mechanism for liability for ship-source oil spills."