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Applied to Model-Based Policy Analysis under Deep Uncertainty 18d ago

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Applied to Speculative scenarios for climate-caused existential catastrophes 2mo ago

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Applied to Why GiveWell should use complete uncertainty quantification 3mo ago

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Applied to Be less trusting of intuitive arguments about social phenomena 3mo ago

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Applied to ‘Dissolving’ AI Risk – Parameter Uncertainty in AI Future Forecasting 5mo ago

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Applied to A model about the effect of total existential risk on career choice 6mo ago

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Applied to Methods for improving uncertainty analysis in EA cost-effectiveness models 7mo ago

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Applied to EAs underestimate uncertainty in cause prioritisation 7mo ago

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Applied to Refuting longtermism with Fermat's Last Theorem 7mo ago

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Applied to Probability distributions of Cost-Effectiveness can be misleading 9mo ago

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Applied to Quantifying Uncertainty in GiveWell's GiveDirectly Cost-Effectiveness Analysis 10mo ago

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Applied to My notes on: Sequence thinking vs. cluster thinking 10mo ago

By plugging in probability distributions or confidence windows, rather than individual estimates, for the values of the parameters in a given model, we can calculate an output for the model that also reflects uncertainty. However, it is important to be careful when performing such calculations, since small mathematical or conceptual errors can easily lead to incorrect or misleading results. A good tool for avoiding these sorts of errors is ~~Guesstimate (Gooen 2015).~~Guesstimate.^{[1]}

~~Gooen, Ozzie (2015) ~~~~Guesstimate: An app for making decisions with confidence (intervals)~~~~, ~~~~Effective Altruism Forum~~~~, December 30.~~

^{^}Gooen, Ozzie (2015) Guesstimate: An app for making decisions with confidence (intervals),

*Effective Altruism Forum*, December 30.

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Applied to Seeking feedback on new EA-aligned economics paper 1y ago

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credence | forecasting | sequence vs. cluster thinking | value of information