• Graduate Programs
    • Facilities
    • Tinbergen Institute Research Master in Economics
      • Why Tinbergen Institute?
      • Research Master
      • Admissions
      • PhD Vacancies
      • Selected PhD Placements
    • Research Master Business Data Science
    • Education for external participants
    • Summer School
    • Tinbergen Institute Lectures
    • PhD Vacancies
  • Research
  • Browse our Courses
  • Events
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
  • Alumni
    • PhD Theses
    • Master Theses
    • Selected PhD Placements
    • Key alumni publications
    • Alumni Community
Home | Events Archive | SEMINAR HAS BEEN CANCELLED
Seminar

SEMINAR HAS BEEN CANCELLED


  • Series
  • Speaker(s)
    Uri Gneezy (UC San Diego, United States)
  • Field
    Behavioral Economics
  • Location
    University of Amsterdam, Roeterseilandcampus
    Amsterdam
  • Date

    June 20, 2023

Improving Human Deception Detection using Algorithmic Feedback

Abstract

Can algorithms help people predict behavior in high-stakes prisoner’s dilemmas? Participants watching the pre-play communication of contestants in the TV show Golden Balls display a limited ability to predict contestants’ behavior, while algorithms do significantly better. We provide participants algorithmic advice by flagging videos for which an algorithm predicts a high likelihood of cooperation or defection. We find that the effectiveness of flags depends on their timing: participants rely significantly more on flags shown before they watch the videos than flags shown after they watch them. These findings show that the timing of algorithmic feedback is key for its adoption.

Sign-up for mailing list: Please email Natalie Lee (h.lee@uva.nl)