• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Recent PhD Placements
    • Admissions
    • Facilities
  • Research
  • News
  • Events
    • Events Calendar
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference
    • Events Archive
    • Summer School
      • Introduction in Genome-Wide Data Analysis
      • Inequalities in Health and Healthcare
      • Crash Course in Experimental Economics
      • Econometric Methods for Forecasting and Data Science
      • Behavioral Macro and Complexity
  • Times
Home | Events Archive | Incentive and Aggregation Mechanisms for Crowdsourcing
Seminar

Incentive and Aggregation Mechanisms for Crowdsourcing


  • Series
    Array
  • Speaker(s)
    Benjamin Tereick, Erasmus University Rotterdam
  • Field
    Behavioral Economics
  • Location
    Erasmus University, Theil-Building, Room C1-6
    Rotterdam
  • Date and time

    November 26, 2019
    13:00 - 14:00

Abstract

I give an overview over different projects from my PhD research. In my job market paper, I investigate empirically and theoretically how to make use of "meta-beliefs” to improve the information aggregation capacity of crowds. I propose a new aggregation scheme, “self-aggregation” (SELF) and show that in a model in which individuals update their beliefs in a Bayesian fashion, SELF is predicted to outperform alternatives. In an experimental test of the model, respondents solve a binary decision problem in a stylized urn experiment, in which responses and aggregation results can be directly compared to the Bayesian prescription. I find that the use of meta-beliefs improves the accuracy of the crowds’ decisions, even though individual answers differ systematically from the Bayesian prescription.

I embed this project in the general aim of my dissertation to facilitate the use of external crowds to improve decision-making. In particular, I discuss an incentive scheme to encourage truthful participation by the crowd members.