On Higher Order Elicitability and Some Limit Theorems on the Poisson and Wiener Space

This PhD thesis consists of two independent parts. The first one is dedicated to a thorough study of higher order elicitability whereas the second part is concerned with qualitative and quantitative limit theorems for Poisson and Gaussian functionals. It comprises a total number of four articles, three of them already published in peer-reviewed journals (Annals of Statistics, Risk Magazine, and ALEA), the fourth one in a preprint version. The articles are accompanied by detailed additional material, primarily concerning questions of order-sensitivity, order-preservingness and convexity of strictly consistent scoring functions

Incentives for Effort in Crowdsourcing using the Peer Truth Serum

Crowdsourcing is widely proposed as a method to solve large variety of judgement tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called {\em peer consistency}. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh (WWW 2013) show that in some cases exerting high effort can be encouraged in the highest-paying equilibrium. In this paper we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties

Facets of forecast evaluation

Forecasts are issued as point or probabilistic predictions, and their performance is measured using consistent scoring functions or proper scoring rules. We identify classes of elementary members of these performance measures and develop diagnostic tools to assist in the ranking of forecasters. Rankings are subject to the choice of evaluation criterion and the sampling variability. We also provide guidance in the computation of a particular scoring rule, the continuous ranked probability score