No Manipulation Results for Non-Bayesian Tests

In Dekel and Feinberg (2004) we suggested a test for discovering whether a potential expert is informed of the distribution of a stochastic process. This category test requires predicting a “small”– category I – set of outcomes. In this paper we show that there is a randomized category test that cannot be manipulated, i.e. such that no matter how the potential expert randomizes his prediction, there will be realizations where he will fail to pass the test with probability 1. The set of outcomes where he fails can be made large – a category II set – under the continuum hypothesis. Moreover, these results hold for the finite approximations of the category tests where the non-expert is failed in finite time and the expert is failed with small probability. JEL Classification: K9