• Graduate Programs
    • Tinbergen Institute Research Master in Economics
      • Why Tinbergen Institute?
      • Research Master
      • Admissions
      • All Placement Records
      • PhD Vacancies
    • Facilities
    • 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

Bleichrodt, H., Cillo, A. and Diecidue, E. (2010). A quantitative measurement of regret theory Management Science, 56(1):161--175.


  • Journal
    Management Science

This paper introduces a method to measure regret theory, a popular theory of decision under uncertainty. Regret theory allows for violations of transitivity, and it may seem paradoxical to quantitatively measure an intransitive theory. We adopt the trade-off method and show that it is robust to violations of transitivity. Our method makes no assumptions about the shape of the functions reflecting utility and regret. It can be performed at the individual level, taking account of preference heterogeneity. Our data support the main assumption of regret theory, that people are disproportionately averse to large regrets, even when event-splitting effects are controlled for. The findings are robust: similar results were obtained in two measurements using different stimuli. The data support the reliability of the trade-off method: its measurements could be replicated using different stimuli and were not susceptible to strategic responding.