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The paper investigates two topics in game theory and decision-making. In the first part, it explores the concept of delegation within a Bayesian persuasion framework. In the second part, the paper focuses on the process of equilibrium selection between the Pareto dominant equilibrium and the risk dominant equilibrium.
This paper outlines the need for AI risk regulation due to documented harms caused by AI systems. It cites examples of proposed and enacted laws aimed at mitigating these risks but highlights challenges in quantifying harms. It criticizes a bias towards technocorrectionism and advocates for a broader regulatory approach to address AI's impacts effectively.
"... the level of insurance knowledge does not influence insurance risk attitudes but does influence the purchase of non-mandatory insurance products."
Analyzing EEA insurers from 2012 to 2021 using a difference-in-differences approach, this study reveals improvements in analysts' forecasts post-Solvency II implementation. Although no change in forecast bias is observed, there is a reduction in absolute earnings forecast errors and forecast dispersion, highlighting the positive impact of Solvency II disclosures on reporting accuracy. The findings contribute to insurance literature and inform regulatory authorities.
Experts agree on the societal benefits of pooling longevity risk through annuities and pensions. While pooling reduces the upfront capital needed for a secure income, challenges arise when participants vary in wealth and health. This paper proposes a model for distributing income in diverse longevity-risk pools, emphasizing the role of social cohesion.
The study introduces partial law invariance, a novel concept extending law-invariant risk measures in decision-making under uncertainty. It characterizes partially law-invariant coherent risk measures with a unique formula, deviating from classical approaches. Strong partial law invariance is introduced, proposing new risk measures like partial versions of Expected Shortfall for risk assessment under model uncertainty.
"This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies' risk management."
This paper addresses the inadequacy of the current U.S. tort liability system in handling the catastrophic risks posed by advanced AI systems. The author proposes punitive damages to incentivize caution in AI development, even without malice or recklessness. Additional suggestions include recognizing AI as an abnormally dangerous activity and requiring liability insurance for AI systems. The paper concludes by acknowledging the limits of tort liability and exploring complementary policies for mitigating catastrophic AI risk.
The study introduces a fraud lexicon and a Balanced Random Forest classifier for detecting fraudulent financial reporting. The classifier, utilizing the fraud lexicon as a feature set, demonstrates strong accuracy in predicting fraud across multiple samples from 2000 to 2017, outperforming random guessing by 40 to 48 percent. The fraud lexicon proves valuable for "bag-of-words" analysis, benefiting researchers, practitioners, auditors, regulators, and investors in enhancing fraud risk assessment procedures.
The paper introduces a new robust estimation technique, the Method of Truncated Moments (MTuM), tailored for estimating the tail index of a Pareto distribution from grouped data. It addresses limitations in existing methods for grouped loss severity data, providing inferential justification through the central limit theorem and simulation studies.