The N-Person Iterated Prisoners’ Dilemma (N-IPD) is an excellent environment to explore collaboration. This paper shows that the voting mechanism is crucial in determining whether sets of agents collaborate, or defect. When each agent can vote against each other agent individually, the agents become cooperative much more easily, ascending the Collaborative Hill. When the agents have only one vote each round, they tend to defect, descending into the Tragic Valley. This is shown with static decision policies, and with policies that learn using reinforcement learning. Fortunately, when agents retain enough history, they can become collaborative even with one vote each round. This voting policy difference is due to the shape of the reward space.