Overfitting and Heuristics in Philosophy
Aug 2024 · Oxford University Press
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In his Rutgers Lectures, Timothy Williamson explains how contemporary philosophy suffers from a widespread pathology known as overfitting to natural and social scientists, but little understood by most philosophers. Overfitting involves an insufficiently critical attitude towards data, which leads to over-complicated theories designed to fit what are in fact errors in the data. In philosophy, the data typically comprise verdicts on hypothetical or actual cases. Errors in such data can result from our reliance on heuristics, efficient cognitive shortcuts, simple to use but not fully reliable. Just as heuristics embedded in our visual system produce visual illusions, so heuristics embedded in our general cognitive systems produce philosophical paradoxes. Williamson explains the heuristics responsible for paradoxes of vagueness and identity over time, paradoxes of conditionals, paradoxes in ascribing beliefs and other mental states to others, paradoxes of truth and falsity, and paradoxes of weighing reasons and intersectionality. As a case study, Williamson shows how illusions of hyperintensionality can result from a heuristic that projects cognitively significant differences in how explanations are presented onto supposed differences in the non-linguistic world, which then form the starting point for metaphysicians' theorizing. In each case, Williamson provides independent evidence that we commonly use the heuristic, and that it sometimes leads us astray. In short, we are being suckered by our own heuristics, and the result is overfitting. Williamson also discusses how philosophers can best avoid these problems. Williamson's important diagnosis and prescription will be of interest to a wide range of philosophers.
In his Rutgers Lectures, Timothy Williamson explains how contemporary philosophy suffers from a widespread pathology known as overfitting to natural and social scientists, but little understood by most philosophers. Overfitting involves an insufficiently critical attitude towards data, which lead...
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