科学知识常困于局部最优，受历史与制度束缚而难达更优解。
The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
- 将科学发现类比为梯度下降，受可操作性与制度激励驱动
- 揭示认知、形式与制度三重锁定机制阻碍范式突破
- 适合关注科学哲学与科研机制改进的研究者阅读
科学被视为探索自然真理最可靠的方法，但其发展轨迹很少被当作优化问题来审视。本文认为，当前科学知识体系本质上是局部最优而非全局最优——我们理解自然的框架、形式与范式，深受历史偶然性、认知路径依赖和制度锁定的影响。类比机器学习中的梯度下降，科学往往沿着最易处理、可实证且获制度奖励的方向前进，从而可能跳过对自然更根本的描述。通过数学、物理、化学、生物、神经科学及统计方法等领域的案例研究，本文识别出三种相互交织的锁定机制：认知、形式与制度。指出认识这些机制是设计元科学策略以跳出局部最优的前提。最后提出具体干预措施，并讨论该论点对科学哲学的启示。
原文摘要 · Abstract (English)
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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