Beyond Bias and Variance: Toward an Asymptotic Epistemology of Embodied Inference

Abstract This paper addresses a fundamental tension in contemporary epistemology: how finite cognitive agents achieve reliable knowledge despite operating under severe informational constraints. Drawing from the statistical learning concept of the bias-variance tradeoff, I argue that human epistemic success depends not on minimizing error through optimal model selection, but on achieving what I term “asymptotic coherence” — a form of epistemic stability that emerges through structured simplification rather than comprehensive accuracy. The term “asymptotic” is employed in its strict sense: perfect epistemic coherence functions as a formal attractor that finite agents approach but cannot reach in principle, given the physical and computational limits inherent to any embodied knower. This challenges both traditional foundationalist approaches that seek certain epistemic foundations and coherentist theories that require comprehensive systematic integration. Instead, I propose that human knowledge is characterized by Simon’s “bounded rationality” — inference strategies that maintain structural coherence across informational insufficiency — understood not as a deficient approximation of ideal rationality but as an irreducibly distinct epistemic form, constituted by the structure of cognitive environments rather than merely constrained by it. This framework draws on embodied and enactive approaches to cognition and offers new insights into long-standing epistemological puzzles about heuristic reasoning, epistemic justification under uncertainty, and the relationship between structural environmental coupling and knowledge acquisition.