special features that deserve further examination: (i) each agent makes a single, irreversible decision; (ii) the timing of the agent's decision(his osition in the decision-making queue)is fixed and exogenous; (ii)agent observe the actions of all their predecessors; and (iv) the number of signals like the number of agents, is infinite. so once a cascade begins the amount of information lost is large. These features simplify the analysis of the SSLM, but they are quite restrictive In this paper, we study the uniformity of behavior in a framework that llows for a richer pattern of social learning. We depart from the SSLM two ways. First, we drop the assumption that actions are public infor- mation and assume that agents can observe the actions of some, but not necessarily all, of their neighbors. Second, we allow agents to make deci sions simultaneously, rather than sequentially, and to revise their decisions rather than making a single, irreversible decision. We refer to this structure as the social network model(sNM). For empirical examples that illustrate he important role of networks in social learning, see Bikhchandani. Hirsh leifer and Welch(1998) On the face of it, uniform behavior seems less likely in the SNM, where agents have very different information sets, than in the SSLM. However uniformity turns out to be a robust feature of connected social networks. The following results are established for any connected network: Uniformity of behavior: Initially, diversity of private information leads to diversity of actions. Over time. as agents learn by observing the actions of their neighbors, some convergence of beliefs is inevitable. A central question is whether agents can rationally choose different actions forever Disconnected agents can clearly 'disagree'forever. Also, there may be cases where agents are indifferent between two actions and disagreement of actions is immaterial. However, apart from cases of disconnectedness and indifference, all agents must eventually choose the same action. Thus, learning occurs through diversity but is eventually replaced by uniformity. Optimality: We are interested in whether the common action chosen asymp- otically is optimal, in the sense that the same action would be chosen if all the signals were public information. In special cases, we can show that asymptotically the optimal action is chosen but, in general. there is no reason why this should be the case. Although the process of learning in networks can be very complicated e SNM has several features that make the asymptotic analysis tractable. The first is the welfare-improvement principle Agents have perfect recall, so expected utility is non-decreasing over time. This implies that equilibri d A network is a directed graph in which the node Agent i can observe the actions of agent j if i is connected to agent 3. A network is onnected if, for any two agents i and j, there is a sequence il,. iK such that il =i. iK=j and ik is connected to ik+1 for k= l,,K-1special features that deserve further examination: (i) each agent makes a single, irreversible decision; (ii) the timing of the agent’s decision (his position in the decision-making queue) is fixed and exogenous; (iii) agents observe the actions of all their predecessors; and (iv) the number of signals, like the number of agents, is infinite, so once a cascade begins the amount of information lost is large. These features simplify the analysis of the SSLM, but they are quite restrictive. In this paper, we study the uniformity of behavior in a framework that allows for a richer pattern of social learning. We depart from the SSLM in two ways. First, we drop the assumption that actions are public infor￾mation and assume that agents can observe the actions of some, but not necessarily all, of their neighbors. Second, we allow agents to make deci￾sions simultaneously, rather than sequentially, and to revise their decisions rather than making a single, irreversible decision. We refer to this structure as the social network model (SNM). For empirical examples that illustrate the important role of networks in social learning, see Bikhchandani, Hirsh￾leifer and Welch (1998). On the face of it, uniform behavior seems less likely in the SNM, where agents have very different information sets, than in the SSLM. However, uniformity turns out to be a robust feature of connected social networks.4 The following results are established for any connected network: Uniformity of behavior : Initially, diversity of private information leads to diversity of actions. Over time, as agents learn by observing the actions of their neighbors, some convergence of beliefs is inevitable. A central question is whether agents can rationally choose different actions forever. Disconnected agents can clearly ‘disagree’ forever. Also, there may be cases where agents are indifferent between two actions and disagreement of actions is immaterial. However, apart from cases of disconnectedness and indifference, all agents must eventually choose the same action. Thus, learning occurs through diversity but is eventually replaced by uniformity. Optimality: We are interested in whether the common action chosen asymp￾totically is optimal, in the sense that the same action would be chosen if all the signals were public information. In special cases, we can show that asymptotically the optimal action is chosen but, in general, there is no reason why this should be the case. Although the process of learning in networks can be very complicated, the SNM has several features that make the asymptotic analysis tractable. The first is the welfare-improvement principle. Agents have perfect recall, so expected utility is non-decreasing over time. This implies that equilibrium 4A network is a directed graph in which the nodes correspond to representative agents. Agent i can observe the actions of agent j if i is connected to agent j. A network is connected if, for any two agents i and j, there is a sequence i1, ..., iK such that i1 = i, iK = j and ik is connected to ik+1 for k = 1, ..., K − 1. 3