“An Epistemic Defense of Rounding Down” by Hayley Clatterbuck

This post is part of WIT's CRAFT sequence. It examines one of the decision theories included in the Portfolio Builder Tool. Executive summary Expected value maximization (EVM) leads to problems of fanaticism, recommending that you ought to take gambles on actions that have very low probabilities of success if the potential outcomes would be extremely valuable. This has motivated some to adopt alternative decision procedures. One common method for moderating the fanatical effects of EVM is to ignore very low probability outcomes, rounding them down to 0. Then, one maximizes EV across the remaining set of sufficiently probable outcomes. We can distinguish between two types of low probabilities that could be candidates for rounding down. A decision-theoretic defense of rounding down states that we should (or are permitted to) round down low objective chances. An epistemic defense states that we should (or are permitted to) round down low [...] ---Outline:(00:20) Executive summary(02:22) 1. Intro(11:06) 2. Rounding down(14:28) 3. Decision-theoretic rounding down(18:10) 4. Objections to decision-theoretic rounding down(18:34) 4.1. Objection 1: Ignores important outcomes(20:24) 4.2. Objection 2: Neglects decisions under the threshold(23:17) 4.3. Objection 3: Normative arbitrariness(26:45) 4.4. Objection 4: The threshold for rounding down is arbitrary(29:36) 5.1. Tolerance(32:04) 5.2. Specificity and Resolution(35:36) 5.3. Amorphousness(37:47) 6. Why round down?(43:45) 7. An epistemic defense of rounding down(50:34) 8. Implications for decisions in EA(52:30) AcknowledgmentsThe original text contained 30 footnotes which were omitted from this narration. The original text contained 2 images which were described by AI. --- First published: July 15th, 2024 Source: https://forum.effectivealtruism.org/posts/N2FQihzG6SW5BfvtS/an-epistemic-defense-of-rounding-down --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.