AGENTIC-SYSTEMSApril 15, 20261 min read15 views

The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap

This paper argues that scientific knowledge represents a local optimum rather than a global one, shaped by historical contingency and institutional lock-in, much like the gradient descent process in machine learning.

Computer Science > Artificial Intelligence

Title:The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap

View PDF HTML (experimental)Abstract:Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the \emph{trajectory} of scientific discovery is rarely examined as an optimization problem in its own right. This paper argues that the body of scientific knowledge, at any given historical moment, represents a \emph{local optimum} rather than a global one--that the frameworks, formalisms, and paradigms through which we understand nature are substantially shaped by historical contingency, cognitive path dependence, and institutional lock-in. Drawing an analogy to gradient descent in machine learning, we propose that science follows the steepest local gradient of tractability, empirical accessibility, and institutional reward, and in doing so may bypass fundamentally superior descriptions of nature. We develop this thesis through detailed case studies spanning mathematics, physics, chemistry, biology, neuroscience, and statistical methodology. We identify three interlocking mechanisms of lock-in--cognitive, formal, and institutional--and argue that recognizing these mechanisms is a prerequisite for designing meta-scientific strategies capable of escaping local optima. We conclude by proposing concrete interventions and discussing the epistemological implications of our thesis for the philosophy of science.

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