<p>Debates over whether Big Data has replaced theory often obscure a deeper epistemological fault line: a shared technocratic pursuit of certainty, whether through statistical regularization or narrative coherence. This paper contends that the quest for epistemic stability can flatten social complexity, with significant democratic consequences. Through a sociotechnical analysis of machine-learning models in Indian agriculture, this paper investigates how predictive <i>success</i> is often attained by converting contingent realities into computationally interpretable forms, a process that can marginalize situated forms of knowledge and normalize algorithmic authority. In response, this paper proposes a framework for engaging with uncertainty not as a flaw to be eradicated but as an inherent epistemic condition. Drawing on Popperian falsifiability (which institutionalizes doubt), Buddhist impermanence (anicca) (which emphasizes continual change), and Confucian relational judgment (ren, shu) (which embeds evaluation in context-sensitive reciprocity), the framework maintains a productive tension among these traditions without synthesizing them into a single unified position. It does not resolve uncertainty but offers evaluative orientations for navigating it in ways that promote epistemic humility, democratic contestation, and resistance to algorithmic dogma and theoretical closure.</p>