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

Computer Science > Artificial Intelligence [Submitted on 11 Apr 2026] The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap Mohamed Mabrok 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. Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Optimization and Control (math.OC) Cite as: arXiv:2604.11828 [cs.AI]   (or arXiv:2604.11828v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.11828 Focus to learn more Submission history From: Mohamed A. Mabrok [view email] [v1] Sat, 11 Apr 2026 17:09:43 UTC (26 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CY math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)