Eliciting and Aggregating Information: An Information Theoretic Approach
- Yuqing Kong, University of Michigan
- Time: 2018-04-02 11:40
- Host: Prof. Baoquan Chen
- Venue: Room 101, Courtyard No.5, Jingyuan
Abstract
Crowdsourcing – outsourcing tasks to a crowd of workers (e.g. Amazon Mechanical Turk, peer grading for massive open online courseware (MOOCs), scholarly peer review, and Yahoo answers) – is a fast, cheap, and effective method for performing simple tasks even at large scales. Two central problems in this area are:
(1) Information Elicitation how to design reward systems that incentivize high quality feedback from agents; and
(2) Information Aggregation how to aggregate the collected feedback to obtain a high quality forecast.
Recently, great process has been made in crowdsourcing, especially in the situation where there is no ground truth. However, the techniques used in this literature are ad hoc and sometimes lack deep intuition. Moreover, the literature also lacks a deep connection between information elicitation and information aggregation.
The combination of game theory and learning theory has made innovative progress (e.g. Generative Adversarial Networks) recently. A central contention of this talk is that the combination of game theory, information theory, and learning theory brings a unied framework to both of the central problems in crowdsourcing area. In this talk, I will introduce several innovative connections among game theory, information theory, and learning theory from my work and show that how to use the connections to build a unified framework and make new progress in crowdsourcing.
Biography
Yuqing Kong is a PhD candidate in University of Michigan (expected to graduate in April 2018) and interested in the intersection of theoretical computer science and the areas of economics: information elicitation, prediction markets, mechanism design, and the future applications of these areas to crowdsourcing and machine learning. She received a Bachelor of Science degree in mathematics from University of Science and Technology of China in 2013.