This was part of Decision Making under Uncertainty


Christopher Chambers, Georgetown University
Wednesday, May 4, 2022

Abstract: An analyst is tasked with producing a statistical study. The analyst is not monitored and is able to manipulate the study. He can receive payments contingent on his report and trusted data collected from an independent source, modeled as a statistical experiment. We describe the information that can be elicited with appropriately shaped incentives, and apply our framework to a variety of common statistical models. We then compare experiments based on the information they enable us to elicit. This order is connected to, but different from, the Blackwell order. Data that is preferred for estimation is also preferred for elicitation, but not conversely. Our results shed light on how using data as incentive generator in payment schemes differs from using data for learning.