In a social network, agents are intelligent and have the capacity to make decisions so as to maximize their utility. They can either make wise decisions by taking advantages of other agents’ experiences through learning or make decisions earlier to avoid competition from huge crowds. Both of these effects – social learning and negative network externality – play important roles in the decision-making process of an agent. In this chapter, a new game called the Chinese restaurant game is introduced to formulate the social learning problem with negative network externality. Through analyzing the Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one application of the Chinese restaurant game. We find that the Chinese restaurant game-theoretic approach indeed helps users make better decisions and improves overall system performance.