  We propose a model for multi-objective optimization, a credo, for agents in a
system that are configured into multiple groups (i.e., teams). Our model of
credo regulates how agents optimize their behavior for the groups they belong
to. We evaluate credo in the context of challenging social dilemmas with
reinforcement learning agents. Our results indicate that the interests of
teammates, or the entire system, are not required to be fully aligned for
achieving globally beneficial outcomes. We identify two scenarios without full
common interest that achieve high equality and significantly higher mean
population rewards compared to when the interests of all agents are aligned.
