The article argues that the Federal Reserve's recent rate hike decision, while justifiable on inflation grounds, highlights a critical lack of an underlying, transparent policy framework. The core problem is that the Fed's decisions appear discretionary, leading to market uncertainty because the committee's judgment, rather than clear data, dictates policy shifts. The author proposes that the Fed adopt a formal monetary policy rule—an algebraic formula linking the target rate to indicators like inflation and unemployment—to replace subjective guidance. Implementing such a rule would provide market predictability, enhance transparency, and shield the Fed from political attacks by making deviations from the standard easily quantifiable.
Infinite Potential—Insights from the Cyber Surprise Scenario: Post-Series Scenario Report from a Sequence of Day After Artificial General Intelligence Exercises
English Summary
RAND's "Cyber Surprise" scenario exercises reveal that U.S. policymakers, when confronted with advanced Chinese cyber-AI capabilities and subsequent large-scale cyberattacks, tend towards aggressive countermeasures and struggle with effective allied engagement. The simulations showed participants advocating for a "use-it-or-lose-it" approach to cyber access against the PRC and grappling with information sharing and coordination with allies. The findings highlight critical needs for enhanced U.S. national preparedness, including developing domestic cyber-AI models, improving whole-of-government and public resilience to cyberattacks, and establishing clear playbooks for engaging adversaries and allies in future AI-driven cyber crises.
中文摘要
美國蘭德公司（RAND）的「網路突襲」情境演習揭示，當美國政策制定者面對中國先進的網路人工智慧（AI）能力及隨之而來的大規模網路攻擊時，傾向採取侵略性反制措施，並在與盟友進行有效合作方面面臨挑戰。模擬演習顯示，參與者主張對抗中華人民共和國時應對網路存取採取「不用即失」的策略，並努力應對與盟友之間的資訊共享與協調問題。這些發現凸顯了美國加強國家備戰的關鍵需求，包括開發國內網路人工智慧模型、提高政府整體和公眾應對網路攻擊的韌性，以及為未來人工智慧驅動的網路危機中與對手和盟友互動制定清晰的行動指南。
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