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Model Uncertainty and Choices Made by Modelers: Lessons Learned from the International Atomic Energy Agency Model Intercomparisons

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  • Igor Linkov
  • Dmitriy Burmistrov

Abstract

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often‐overlooked issue of what we call “modeler uncertainty,” i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model‐model and model‐data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic‐deliberative process in risk characterization.

Suggested Citation

  • Igor Linkov & Dmitriy Burmistrov, 2003. "Model Uncertainty and Choices Made by Modelers: Lessons Learned from the International Atomic Energy Agency Model Intercomparisons," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1297-1308, December.
  • Handle: RePEc:wly:riskan:v:23:y:2003:i:6:p:1297-1308
    DOI: 10.1111/j.0272-4332.2003.00402.x
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    References listed on IDEAS

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    1. Elizabeth A. Casman & M. Granger Morgan & Hadi Dowlatabadi, 1999. "Mixed Levels of Uncertainty in Complex Policy Models," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 33-42, February.
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    1. Milad Eghtedari Naeini & Benjamin D. Leibowicz & J. Eric Bickel, 2020. "Can you trust a model whose output keeps changing? Interpreting changes in the social cost of carbon produced by the DICE model," Environment Systems and Decisions, Springer, vol. 40(3), pages 301-320, September.
    2. Giuseppe Aiello & Salvatore Quaranta & Rosalinda Inguanta & Antonella Certa & Mario Venticinque, 2024. "A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty," Energies, MDPI, vol. 17(6), pages 1-19, March.
    3. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
    4. Iftikhar Ahmad & Ahsan Ayub & Uzair Ibrahim & Mansoor Khan Khattak & Manabu Kano, 2018. "Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process," Energies, MDPI, vol. 12(1), pages 1-13, December.
    5. Judson Jaffe & Robert N. Stavins, 2007. "On the value of formal assessment of uncertainty in regulatory analysis," Regulation & Governance, John Wiley & Sons, vol. 1(2), pages 154-171, June.
    6. Vicki M. Bier & Shi‐Woei Lin, 2013. "On the Treatment of Uncertainty and Variability in Making Decisions About Risk," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1899-1907, October.
    7. Edoardo Tosoni & Ahti Salo & Enrico Zio, 2018. "Scenario Analysis for the Safety Assessment of Nuclear Waste Repositories: A Critical Review," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 755-776, April.
    8. Zachary A. Collier & James H. Lambert & Igor Linkov, 2020. "Concurrent threats and disasters: modeling and managing risk and resilience," Environment Systems and Decisions, Springer, vol. 40(3), pages 299-300, September.
    9. Roxane Foulser‐Piggott & Gary Bowman & Martin Hughes, 2020. "A Framework for Understanding Uncertainty in Seismic Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 169-182, January.
    10. Alberto Garre & Geraldine Boué & Pablo S. Fernández & Jeanne‐Marie Membré & Jose A. Egea, 2020. "Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 336-351, February.

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