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2 posts found
May 17, 2015
ssc
9 min 1,249 words 485 comments
Scott Alexander explores 'bicameral reasoning', comparing how we often weigh issues equally (like the US Senate) instead of proportionally to their importance (like the House), leading to potentially skewed decision-making. Longer summary
This post discusses the concept of 'bicameral reasoning', drawing parallels between the US House and Senate representation systems and how people make decisions or judgments. Scott Alexander argues that often we give equal weight to issues of vastly different importance, much like how the Senate gives equal representation to states regardless of population. He illustrates this with examples from political issues, animal welfare considerations, and environmental concerns. The post suggests that this 'Senate-like' thinking can lead to poor decision-making by equating minor issues with major ones. While acknowledging some potential benefits to this way of thinking in extreme cases, the author ultimately argues for a more proportional 'House-like' approach to evaluating issues based on their actual impact or importance. Shorter summary
May 02, 2013
ssc
11 min 1,538 words 65 comments
Scott Alexander argues for the value of using quantification and made-up statistics in decision-making, even when imperfect, as they often outperform intuition and reveal biases in our thinking. Longer summary
Scott Alexander discusses the value of using made-up statistics and quantification in decision-making, even when the numbers are imperfect. He argues that this approach can often lead to better outcomes than relying solely on intuition or System 1 thinking. The post begins with an anecdote about teaching Bayes' Theorem, then explores how quantification can improve decision-making in various fields, including utilitarianism and medical diagnosis. Scott emphasizes that while these numbers may be imperfect, they often provide more accurate results than gut feelings, which can be severely biased. He concludes by advocating for applying made-up models to various problems as a way to challenge our intuitions and gain new perspectives. Shorter summary