Scott Alexander argues against significantly updating beliefs based on single dramatic events, advocating for consistent policies based on pre-existing probability distributions.
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Scott Alexander argues against dramatically updating one's beliefs based on single events, even if they are significant. He contends that a good Bayesian should have distributions for various events and only make small updates when they occur. The post covers several examples, including COVID-19 origin theories, 9/11, mass shootings, sexual harassment scandals, and crises in the effective altruism movement. Scott suggests that while dramatic events can be useful for coordination and activism, they shouldn't significantly alter our understanding of underlying probabilities. He advocates for predicting distributions beforehand and maintaining consistent policies rather than overreacting to individual incidents.
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