In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents’ experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both these two contradictory effects is stil l limited. We find that the Chinese restaurant process, a popular random process, provides a well-defined s tructure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, in Part I of this two-part paper, we prop ose a new game, called Chinese Restaurant Game, to formulate the social learning problem with negative network externality. Through analyzing the proposed 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 also studied through si mulations.
Angeletos studied a specific dynamic global game called regime of changes @cite_24 @cite_13 . In the regime of changes game, each agent may propose an attack to the status quo, i.e., the current politic state of the society. When the collected attacks are large enough, the status quo is abandoned and all attackers receive positive payoffs. If the status quo does not change, the attackers receive negative payoffs. Angeletos first studied a signaling model with signals at the beginning of the game in @cite_24 . Then, they proposed a multiple stages dynamic game to study the learning behaviors of agents in the regime of change game in @cite_13 .
@cite_24 @cite_13
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This paper introduces signaling in a global game so as to examine the informational role of policy in coordination environments such as currency crises and bank runs. While exogenous asymmetric information has been shown to select a unique equilibrium, we show that the endogenous information generated by policy interventions leads to multiple equilibria. The policy maker is thus trapped into a position in which self-fulfilling expectations dictate not only the coordination outcome but also the optimal policy. This result does not rely on the freedom to choose out-of-equilibrium beliefs, nor on the policy being a public signal; it may obtain even if the policy is observed with idiosyncratic noise. Global games of regime change-coordination games of incomplete information in which a status quo is abandoned once a sufficiently large fraction of agents attack it-have been used to study crises phenomena such as currency attacks, bank runs, debt crises, and political change. We extend the static benchmark examined in the literature by allowing agents to take actions in many periods and to learn about the underlying fundamentals over time. We first provide a simple recursive algorithm for the characterization of monotone equilibria. We then show how the interaction of the knowledge that the regime survived past attacks with the arrival of information over time, or with changes in fundamentals, leads to interesting equilibrium properties. First, multiplicity may obtain under the same conditions on exogenous information that guarantee uniqueness in the static benchmark. Second, fundamentals may predict the eventual fate of the regime but not the timing or the number of attacks. Finally, equilibrium dynamics can alternate between phases of tranquility-where no attack is possible-and phases of distress-where a large attack can occur-even without changes in fundamentals. Copyright The Econometric Society 2007.