Researchers have revisited the 'Sleeping Beauty problem', a thought experiment in decision-making under uncertainty. The problem involves Sleeping Beauty, who is put into a mysterious situation and asked to make bets on her own probability of being awake or asleep. A common approach to solving this problem assumes that Sleeping Beauty should maximize the expected value of her bets. However, a new study shows that this assumption may be flawed, and that Sleeping Beauty's optimal betting strategy is actually identical whether she is trying to gain wealth or avoid losing it. --- Why it matters: This work matters because it challenges a common approach to decision-making under uncertainty in AI research. Understanding the limitations of this approach can help researchers develop more accurate models for making decisions in complex, uncertain environments. Source: https://arxiv.org/abs/2510.15911