In biomolecular regulation, canalization (a term coined by Conrad Waddington to refer to the buffering of genetic, epigenetic, and environmental fluctuations) plays a key role in establishing a robust mapping from genotype to phenotype in noisy environments. Robustness of sensors to fluctuations, requires collective canalization realized with at least two types of dynamical redundancy: 1) heterogenous, nonlinear effective connectivity, whereby most regulatory inputs have little or no effect on downstream signaling and regulation, and 2) symmetry, whereby groups of inputs are causally equivalent. Biologically, this manifests in several ways, including:  multiple signaling pathways, multiple combinations of transcription factors that bind a gene’s promoter region, and threshold behaviors that allow depletion of one signal to be overcome by overabundance of another. We measure effective connectivity and symmetry in experimentally-validated automata network models of biochemical pathways, and demonstrate that these forms of dynamical redundancy are ubiquitous in regulation and signaling. Indeed, the redundancy found in biological models is much more pronounced than what is expected from random networks, and, as we demonstrate, a major reason for them being much more ordered than what the current criticality hypothesis, or “edge-of-chaos” theory, predicts.  The observed redundancy is furthermore shown to shape the controllability and dynamical modularity of biochemical regulation and signaling networks. We also summarize recent computational evolution experiments showing that the necessity of collective decision in noisy environments---e.g. quorum sensing in bacteria or collective intelligence in societies---leads to the evolution of both types of redundancy. As a practical benefit of these results, we show that the observed widespread redundancy in biochemical regulation facilitates the construction of causal models, such as large-scale digital twins for precision medicine. Finally, we discuss how the observed evolution of redundancy constitutes a “selective loss of detail”, which Howard Pattee argues to be required for emergence of new functions in living systems and abstraction in intelligence, and which, we argue, facilitates explanation and prediction in scientific models.