Group formation and collective action are fundamental to cooperative agents seeking to maximize resource growth. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. In this talk, we derive general statistical dynamics for the origin of growth and cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents’ signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. We then show how heterogeneity across groups drives resource inequality, which can be mitigated across similar groups through learning in shared environments. These results show how the general properties of information underlie optimal collective formation and drive the emergence of inequality in social systems.