Roth, Aaron and Tolbert, Alexander (2025) Resolving the Reference Class Problem at Scale. [Preprint]
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Text (Paper forthcoming in Philosophy of Science)
Resolving_the_Reference_Class_Problem_At_Scale.pdf - Accepted Version Available under License Creative Commons Attribution. Download (326kB) |
Abstract
We draw a distinction between the traditional reference class problem which describes an obstruction to estimating a single individual probability---which we re-term the individual reference class problem---and what we call the reference class problem at scale, which can result when using tools from statistics and machine learning to systematically make predictions about many individual probabilities simultaneously. We argue that scale actually helps to mitigate the reference class problem, and purely statistical tools can be used to efficiently minimize the reference class problem at scale, even though they cannot be used to solve the individual reference class problem.
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| Item Type: | Preprint | |||||||||
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| Additional Information: | Forthcoming in Philosophy of Science | |||||||||
| Keywords: | Reference Class Problem, Machine Learning, Individual Probabilities, Calibration, Probability | |||||||||
| Subjects: | Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Probability/Statistics |
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| Depositing User: | Aaron Roth | |||||||||
| Date Deposited: | 21 Mar 2025 13:12 | |||||||||
| Last Modified: | 21 Mar 2025 13:12 | |||||||||
| Item ID: | 23589 | |||||||||
| Subjects: | Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Probability/Statistics |
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| Date: | 20 March 2025 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/23589 |
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