As such, in games where the marginalization of CCE coincides with the game's Nash equilibria (like

In this paper, we examine the long-run behavior of regularized, no-regret learning in finite games. A well-known result in the field states that the empirical frequencies of no-regret play converge to the game's set of coarse correlated equilibria; however, our understanding of how the players' actual strategies evolve over time is much more limited - and, in many cases, non-existent. This issue is exacerbated further by a series of recent results showing that only strict Nash equilibria are stable and attracting under regularized learning, thus making the relation between learning and pointwise solution concepts particularly elusive. In lieu of this, we take a more general approach and instead seek to characterize the \emph{setwise} rationality properties of the players' day-to-day play. To that end, we focus on one of the most stringent criteria of setwise strategic stability, namely that any unilateral deviation from the set in question incurs a cost to the deviator - a property known as closedness under better replies (club). In so doing, we obtain a far-reaching equivalence between strategic and dynamic stability: a product of pure strategies is closed under better replies if and only if its span is stable and attracting under regularized learning. In addition, we estimate the rate of convergence to such sets, and we show that methods based on entropic regularization (like the exponential weights algorithm) converge at a geometric rate, while projection-based methods converge within a finite number of iterations, even with bandit, payoff-based feedback.

Artificial Intelligence (AI) has proved to be a path-breaking experience in many fields in the contemporary world. AI is catching the attention of defense professionals, policymakers, entrepreneurs, and multinational corporations around the globe. The pioneer of AI, John McCarthy, defines it as "the science and engineering of making intelligent machines, brilliant computer programs." The capacity of AI generally refers to the ability of machines to outperform human actions in terms of intelligence, judgment, autonomy, and knowledge discovery. AI has the potential to develop software applications based on self-learning that replicates the qualities of the human mind, resembling decision-making, problem-solving, reasoning, planning, etc.

A new RAND Corporation paper finds that artificial intelligence has the potential to upend the foundations of nuclear deterrence by 2040. While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper. During the Cold War, the condition of mutual assured destruction (MAD) maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. MAD thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war. The new RAND publication says that in the coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability.

A new RAND Corporation paper finds that artificial intelligence has the potential to upend the foundations of nuclear deterrence by the year 2040. While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper. During the Cold War, the condition of mutual assured destruction maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. Mutual assured destruction thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war. The new RAND publication says that in coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability.

While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper. During the Cold War, the condition of mutual assured destruction maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. Mutual assured destruction thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war. The new RAND publication says that in coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability. Improved sensor technologies could introduce the possibility that retaliatory forces such as submarine and mobile missiles could be targeted and destroyed.

A devastating nuclear war that wipes out humanity could be brought about by artificial intelligence as soon as 2040, a leading security think tank has warned. Advances leading to'doomsday AI' machines could encourage nations to take apocalyptic risks with their nuclear arsenals, it claims. During the Cold War, a condition called mutually assured destruction (Mad) maintained an uneasy peace between the superpowers. Both sides of the conflict had little incentive to launch a nuclear attack, as it would have been met by a devastating retaliation that destroyed them. However, advances in AI may mean that this is no longer guaranteed, opening up the potential of taking out an enemy's ability to counter attack - and there may be no way to prevent such a future.

A new RAND Corporation paper finds that artificial intelligence has the potential to upend the foundations of nuclear deterrence by the year 2040. While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper. During the Cold War, the condition of mutual assured destruction maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. Mutual assured destruction thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war. The new RAND publication says that in coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability.