Learning in networks with idiosyncratic agents
研究个体在更新信念时的异质性（如反应不足、过度反应或挫败感）如何通过网络产生溢出效应，影响社会长期信念的收敛性和准确性，并推导出信念收敛的充分条件。
Individuals update their beliefs and respond to new information in idiosyncratic ways. I show that an individual's idiosyncrasies such as under-reaction, over-reaction, or frustration can have spillover effects and adversely affect the long run beliefs of society. I derive sufficient conditions for convergence of beliefs for all possible networks of agents with heterogeneous idiosyncrasies. Beliefs converge if the magnitude of over-reaction and frustration in any agent's network neighbourhood is below a threshold determined by how much they trust their own private signals. I find that the absence of disproportionately influential agents is not sufficient to ensure the accuracy of long-run beliefs if learning idiosyncrasies also grow with the network. Finally, I show that agent under-reaction can partition the network, create bottlenecks, and delay convergence. Simulations on artificial and Indian village networks validate the results.