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Inferences from alarming events.

J W Pratt, R J Zeckhauser

    Journal of Policy Analysis and Management : [The Journal of the Association for Public Policy Analysis and Management]
    |January 6, 1983
    PubMed
    Summary
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    When extreme events occur, estimating past risks is hard due to missing data. This study introduces a probability model to infer event risks using only the first observed event magnitude and exposure, even with unknown thresholds, preventing overestimation.

    Area of Science:

    • Risk assessment
    • Probability modeling
    • Statistical inference

    Background:

    • Extreme events (e.g., nuclear accidents, drug reactions) often highlight risks only after they occur.
    • Historical data on event incidence and magnitude may be lost or prohibitively expensive to recover.
    • Assessing risk accurately is crucial for preparedness and mitigation.

    Purpose of the Study:

    • To develop statistical inference methods for estimating event risks when historical data is unavailable.
    • To provide a method that relies solely on the magnitude of the first observed event and prior exposure.
    • To address the challenge of unknown detection thresholds for events.

    Main Methods:

    • Utilized a simple probability model to derive inference methods.

    Related Experiment Videos

  • Developed techniques for statistical inference based on the first observed event's magnitude.
  • Incorporated the amount of exposure preceding the first event into the model.
  • Accounted for unknown thresholds at which events become noticeable.

    • Main Results:

    • The proposed methods allow for statistical inference using limited data (first event magnitude and exposure).
    • The model provides valid inferences even when the event detection threshold is unknown.
    • Incorrectly assuming the observed event magnitude as the threshold leads to substantial biases, overstating risks up to threefold.

    Conclusions:

    • The developed probability model offers a viable approach to estimate risks from extreme events with incomplete historical data.
    • Accurate risk assessment is possible by considering the first observed event and exposure, even with unknown thresholds.
    • Avoiding common inferential errors prevents significant overestimation of risks, ensuring more reliable safety assessments.