Mathematical models of social learning
Mathematical models of social learning aim to model opinion dynamics in social networks. Consider a social network in which people (agents) hold a belief or opinion about the state of something in the world, such as the quality of a particular product, the effectiveness of a public policy, or the reliability of a news agency. In all these settings, people learn about the state of the world via observation or communication with others. Models of social learning try to formalize these interactions to describe how agents process the information received from their friends in the social network. Some of the main questions asked in the literature include: whether agents reach a consensus; whether social learning effectively aggregates scattered information, or put differently, whether the consensus belief matches the true state of the world or not; how effective media sources, politicians, and prominent agents can be in belief formation of the entire network. In other words, how much room is there for belief manipulation and misinformation? Bayesian learning Bayesian learning is a model which assumes that agents update their beliefs using Bayes' rule. Indeed, each agent's belief about different states of the world can be seen as a probability distribution over