The Taub Faculty of Computer Science Events and Talks

We study the problem of testing an expert who is required to submit forecasts that have a learnable parametric representations. The class of stochastic processes with such representations, introduced by Jackson, Kalai and Smorodinsky (1999), include exchangeable and Markov process as special cases, and encompasses all processes used in Bayesian statistics. We define a simple test that screens experts whose forecasts belong to this class. The test stipulates an initial phase during which the expert may choose to learn from data. At the end of this phase, the expert's conditional forecasts are tested according to a simple frequentist criterion. We show that this test passes an informed expert, but that it cannot be strategically manipulated by an uninformed one.