The definition of emergence remains problematic, particularly for systems with purposeful human interactions. This study explores the practical import of this concept within a specific market context: namely, a double-auction market for wholesale electric power that operates over a transmission grid with spatially located buyers and sellers. Each profit-seeking seller is a learning agent that attempts to adjust its daily supply offers to its best advantage. The sellers are co-learners in the sense that their supply offer adjustments are in response to past market outcomes that reflect the past supply offer choices of all sellers. Attention is focused on the emergence of co-learning patterns, that is, global market patterns that arise and persist over time as a result of seller co-learning. Examples of co-learning patterns include correlated seller supply offer behaviors and correlated seller net earnings outcomes. Heat maps are used to display and interpret co-learning pattern findings. One key finding is that co-learning strongly matters in this auction market environment. Sellers that behave as Gode-Sunder budget-constrained zero-intelligence agents, randomly selecting their supply offers subject only to a break-even constraint, tend to realize substantially lower net earnings than sellers that tacitly co-learn to correlate their supply offers for market power advantages.