  In Part I of this two-part paper [1], we proposed a new game, called Chinese
restaurant game, to analyze the social learning problem with negative network
externality. The best responses of agents in the Chinese restaurant game with
imperfect signals are constructed through a recursive method, and the influence
of both learning and network externality on the utilities of agents is studied.
In Part II of this two-part paper, we illustrate three applications of Chinese
restaurant game in wireless networking, cloud computing, and online social
networking. For each application, we formulate the corresponding problem as a
Chinese restaurant game and analyze how agents learn and make strategic
decisions in the problem. The proposed method is compared with four
common-sense methods in terms of agents' utilities and the overall system
performance through simulations. We find that the proposed Chinese restaurant
game theoretic approach indeed helps agents make better decisions and improves
the overall system performance. Furthermore, agents with different decision
orders have different advantages in terms of their utilities, which also
verifies the conclusions drawn in Part I of this two-part paper.
