While I do agree with your premise on arithmetic, the more valuable tools are arithmetic-adjacent. I am thinking of game theory, Bayesian reasoning, probability, expected value, decision modeling, and so on. This is closer to algebra and high school math, but still pretty accessible. See this post.
The main reason why people struggle with applying arithmetic to world modeling is because transfer learning is really difficult, and EAs/rationalists are much better at applying transfer learning than the regular person. I notice this in my EA group: students who are engineers and aced differential equations and random variables quite struggle with Bayesian reasoning, even though they learned Bayes’ theorem.
Thanks for the comment, Patrick! That makes sense. I suspect status quo bias is an important blocker to transfer learning with respect to applying arithmetic and adjacent methods to figure out how to improve the world. Quantification is unquestionable in engineering projects, but often absent in charitable projects. There is quantification in the effective altruism community, but people still like to conform to societal norms about what it means to contribute to a better world, including about which methods are legitimate to evaluate interventions.
While I do agree with your premise on arithmetic, the more valuable tools are arithmetic-adjacent. I am thinking of game theory, Bayesian reasoning, probability, expected value, decision modeling, and so on. This is closer to algebra and high school math, but still pretty accessible. See this post.
The main reason why people struggle with applying arithmetic to world modeling is because transfer learning is really difficult, and EAs/rationalists are much better at applying transfer learning than the regular person. I notice this in my EA group: students who are engineers and aced differential equations and random variables quite struggle with Bayesian reasoning, even though they learned Bayes’ theorem.
Thanks for the comment, Patrick! That makes sense. I suspect status quo bias is an important blocker to transfer learning with respect to applying arithmetic and adjacent methods to figure out how to improve the world. Quantification is unquestionable in engineering projects, but often absent in charitable projects. There is quantification in the effective altruism community, but people still like to conform to societal norms about what it means to contribute to a better world, including about which methods are legitimate to evaluate interventions.