[2026 University Libraries Research Award Winner]
Modern information systems operate within environments of overwhelming scale, speed, and complexity. Data emerges from diverse sources at rates far exceeding the capacity of any individual observer to fully process. To manage this uncertainty, both human cognition and algorithmic machine learning systems rely on mechanisms that compress informational complexity into more predictable structures. I examine conceptual links between statistical reasoning, Bayesian inference, and lastly Shannon entropy to view how uncertainty is reduced in practice. Particular focus is given to the role of baseline assumptions, which shape inference before analysis formally begins. Bias is examined as a structural consequence of entropy reduction within current constrained systems of interpretation. As systems favor predictable patterns, information diversity declines and feedback loops emerge. The discussion refines by suggesting that maintaining some degree of high-entropy information is needed for preserving novelty, supported through proposing methods of injecting entropy into data in an adaptive way based on a defined entropy threshold for discrete data and formalizing entropy depletion as a concept.
