The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915937640251392 |
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| author | Mabrok, Mohamed |
| author_facet | Mabrok, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11828 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap Mabrok, Mohamed Artificial Intelligence Computers and Society Optimization and Control 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. |
| title | The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap |
| topic | Artificial Intelligence Computers and Society Optimization and Control |
| url | https://arxiv.org/abs/2604.11828 |