AGENTIC-SYSTEMSApril 7, 20261 min read12 views

Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away

This paper explores how modern Bayesian models and LLMs capture statistical updating but fail to satisfy the three essential representational conditions for causal judgment proposed by David Hume.

Computer Science > Artificial Intelligence

Title:Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away

View PDF HTML (experimental)Abstract:Hume's account of causal judgment presupposes three representational conditions: experiential grounding (ideas must trace to impressions), structured retrieval (association must operate through organized networks exceeding pairwise connection), and vivacity transfer (inference must produce felt conviction, not merely updated probability). This paper extracts these conditions from Hume's texts and argues that they are integral to his causal psychology. It then traces their fate through the formalization trajectory from Hume to Bayesian epistemology and predictive processing, showing that later frameworks preserve the updating structure of Hume's insight while abstracting away these further representational conditions. Large language models serve as an illustrative contemporary case: they exhibit a form of statistical updating without satisfying the three conditions, thereby making visible requirements that were previously background assumptions in Hume's framework.

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Source: arXiv cs.AI Recent

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