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Related Experiment Video

Updated: May 20, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Why risk analysis is difficult, and some thoughts on how to proceed.

Yakov Ben-Haim1

  • 1Faculty of Mechanical Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel. yakov@technion.ac.il

Risk Analysis : an Official Publication of the Society for Risk Analysis
|July 6, 2012
PubMed
Summary

Risk analysis faces challenges from evolving knowledge, inherent indeterminism, and untestable assumptions. Robust decision-making strategies, like info-gap analysis, offer effective ways to navigate these uncertainties in technology and opportunity assessment.

Related Experiment Videos

Last Updated: May 20, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Area of Science:

  • Decision Science
  • Risk Management
  • Epistemology

Background:

  • Uncertainty poses significant challenges to traditional risk analysis.
  • These challenges stem from evolving understanding, inherent indeterminism, and untestable assumptions in learning.
  • Existing risk assessment models often struggle with deep uncertainty.

Purpose of the Study:

  • To explore concepts of robustness as a response to epistemological challenges in risk analysis.
  • To justify the use of models despite their known limitations.
  • To compare different robustness concepts and their application to uncertain opportunities.

Main Methods:

  • Conceptual analysis of robustness in decision-making.
  • Comparison of info-gap and worst-case robustness frameworks.
  • Illustrative example of technology choice under uncertainty.

Main Results:

  • Robustness provides a framework for decision-making under uncertainty.
  • Nonprobabilistic robust decisions can be effective probabilistic strategies.
  • Robustness is crucial for exploiting uncertain opportunities.

Conclusions:

  • Robustness concepts offer valuable tools for navigating complex uncertainties in risk analysis.
  • Models remain useful in risk analysis, even with inherent inaccuracies.
  • Strategic decision-making must account for and leverage uncertainty.