More formally, every machine learning algorithm depends on 3 things which need to be able to be programmed. First, there needs to exist an experience set, sometimes called a training set. This is data that the algorithm will “learn” from. Next, there needs to be some task, some action that...

We study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions....

Decisions under ambiguity depend on the beliefs regarding possible scenarios and the attitude towards ambiguity. This paper exclusively focuses on beliefs, and beliefs are measured independently from attitudes, in contrast to many previous studies. We use laboratory experiments to estimate the...