Learning in Social Networks among Biased and Frustrated Agents

Individuals are slow to update their beliefs and may respond to new information in idiosyncratic ways. Since their beliefs affect the choices of those they are linked with, the biases and idiosyncrasies that affect their capacity to learn information also affect information accumulation across society. I study how an individual’s slowness to respond to new information (due to status quo bias) and idiosyncratic ways of responding to new information (due to frustration) affect (a) the ability of society to reach an agreement (b) the ability of society to reach the correct agreement and© the speed with which such an agreement is reached. I derive sufficient conditions for convergence in beliefs in the form of network dependent upper bounds on biases for all networks of individuals with heterogeneous biases, placing heterogeneous weights on their neighbours. Contrary to existing literature, I find that the absence of perpetually influential agents is not a sufficient condition to ensure that beliefs converge to the truth. Finally, I show how biases affect the speed with which societies learn. I derive the relationship between individual biases and learning bottlenecks for different types of networks and illustrate how this can be used to understand and tackle norm persistence.