• Graduate Program
    • Why study Business Data Science?
    • Program Outline
    • Courses
    • Course Registration
    • Admissions
    • Facilities
  • Research
  • News
  • Summer School
    • Deep Learning
    • Machine Learning for Business
    • Tinbergen Institute Summer School Program
    • Receive updates
  • Events
    • Events Calendar
    • Events archive
    • Summer school
      • Deep Learning
      • Machine Learning for Business
      • Tinbergen Institute Summer School Program
      • Receive updates
    • Conference: Consumer Search and Markets
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference archive
  • Alumni

Baillon, A., Halevy, Y. and Li, C. (2022). Randomize at Your Own Risk: On the Observability of Ambiguity Aversion Econometrica, 90(3):1085--1107.


  • Affiliated authors
    Aurélien Baillon, Chen Li
  • Publication year
    2022
  • Journal
    Econometrica

Facing several decisions, people may consider each one in isolation or integrate them into a single optimization problem. Isolation and integration may yield different choices, for instance, if uncertainty is involved, and only one randomly selected decision is implemented. We investigate whether the random incentive system in experiments that measure ambiguity aversion provides a hedge against ambiguity, making ambiguity-averse subjects who integrate behave as if they were ambiguity neutral. Our results suggest that about half of the ambiguity averse subjects integrated their choices in the experiment into a single problem, whereas the other half isolated. Our design further enables us to disentangle properties of the integrating subjects' preferences over compound objects induced by the random incentive system and the choice problems in the experiment.