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Related Experiment Video

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Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

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Published on: June 23, 2018

Risk aggregation considering probabilistic and consequential interactions: A general formulation with computational

Chunbing Bao¹, Meng Cai¹, Jianping Li²

¹School of Management, Shandong University, Jinan, Shandong, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|November 16, 2023

Summary

This summary is machine-generated.

This study introduces a novel risk aggregation method that accounts for complex interactions between risk events. This approach enhances accuracy in risk management, particularly in subjective assessments.

Keywords:

approximate risk measure consequential interaction probabilistic interaction risk aggregation

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Area of Science:

Risk Management
Decision Analysis
Quantitative Finance

Background:

Accurate risk aggregation is crucial for effective risk management.
Existing methods struggle with complex interactions between risk events, limiting universal application.
Subjective risk assessment contexts present unique challenges for current aggregation techniques.

Purpose of the Study:

To propose a theoretically sound risk aggregation method incorporating diverse interaction types.
To enhance the universality and effectiveness of risk aggregation for decision analysis.
To address limitations in current approaches, especially within subjective risk assessment.

Main Methods:

Developing rigorous definitions, measures, and graphical representations for various interaction types.
Formulating a generalized risk aggregation approach applicable to both objective and subjective contexts.
Analyzing the additivity of risks and risk sets, clarifying conditions for additivity.
Introducing quasi-two/three-additive measures to reduce computational costs while maintaining reliability.

Main Results:

The proposed method provides a comprehensive framework for risk aggregation with interaction.
Enhanced accuracy and applicability across objective and subjective risk assessment scenarios.
Clarified conditions for risk additivity and introduced computationally efficient approximation methods.
Demonstrated practical utility through a detailed case study.

Conclusions:

The developed risk aggregation method effectively integrates complex interactions, improving accuracy.
The approach offers a universal solution applicable to diverse risk assessment contexts.
Quasi-additive measures provide a practical means to reduce computational burden without sacrificing reliability.