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

This paper argues that scientific knowledge constitutes a local optimum shaped by historical contingency and institutional lock-in, rather than a global optimum. Drawing an analogy to gradient descent in machine learning, it proposes that science may bypass superior descriptions of nature by following local gradients of tractability and reward.