Abstract

This paper introduces an objective for optimizing proper scoring rules. The objective is to maximize the increase in payoff of a forecaster who exerts a binary level of effort to refine a posterior belief froma prior belief. In this framework we characterize optimal scoring rules in simple settings, give efficient algorithms for computing optimal scoring rules in complex settings, and identify simple scoring rules thatare approximately optimal. In comparison, standard scoring rules in theory and practice – for example, the quadratic rule, scoring rules for the expectation, and scoring rules for multiple tasks that are averages of single-task scoring rules – can be very far from optimal.

Biography

Yifan Wu, Turing Class. Her research interests include algorithmic game theory and mechanism design.

Admission: https://www.wjx.cn/m/85749221.aspx
Meeting Room: https://zoom.com.cn/j/97905195781