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Clarifying Types of Uncertainty: When Are Models Accurate, and Uncertainties Small?
Author(s) -
Cox, Jr. Louis Anthony Tony
Publication year - 2011
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2011.01706.x
Subject(s) - uncertainty quantification , risk analysis (engineering) , precautionary principle , uncertainty analysis , risk management , meaning (existential) , computer science , sensitivity analysis , management science , economics , machine learning , epistemology , business , ecology , philosophy , management , biology , simulation
Professor Aven has recently noted the importance of clarifying the meaning of terms such as “scientific uncertainty” for use in risk management and policy decisions, such as when to trigger application of the precautionary principle. This comment examines some fundamental conceptual challenges for efforts to define “accurate” models and “small” input uncertainties by showing that increasing uncertainty in model inputs may reduce uncertainty in model outputs; that even correct models with “small” input uncertainties need not yield accurate or useful predictions for quantities of interest in risk management (such as the duration of an epidemic); and that accurate predictive models need not be accurate causal models.