Out-Of-Distribution Thinking: Philosophical Insights From Machine Learning
Abstract: Why do people struggle to agree on metaphysical questions? In this talk, I propose the hypothesis that metaphysical reasoning in humans resembles "out-of-distribution" (OOD) generalisation in machine learning (ML). According to this hypothesis, our conceptual structures are well-enough aligned for practical purposes because there are strong external pressures (from embodied experience and social context) towards convergence in our everyday conceptual practices. Unfortunately, our conceptual practices in metaphysical domains are only weakly constrained by those forces; in consequence, sophisticated thinkers can disagree profoundly (and apparently irresolvably) on highly abstract conceptual questions.
This hypothesis has pragmatic consequences for how we approach philosophy. In particular, if our concepts of "truth" and "reality" turn out to be underconstrained when applied in a metaphysical domain, then there is nothing to reliably anchor metaphysical disagreements to, at least if we see them as candidates for truth evaluation. This might incline some to eliminativism about metaphysics, but I argue instead for adopting a flexible, pluralistic approach.
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