Complexity for complexity-How advanced modeling may limit its applicability for decision-makers.

Area of Science:

• Engineering Systems Analysis
• Sociotechnical Systems Safety

Background:

• Modern engineering systems are increasingly complex, challenging traditional safety assessment methods.
• Classical failure analysis techniques (e.g., fault trees, FMEA) create models that may not reflect real-world emergent behaviors.
• The depth of causal chains in serious accidents is often overestimated.

Purpose of the Study:

• To question the necessity of complex decompositional models for safety-critical systems.
• To explore the benefits of whole system modeling for understanding system behavior.
• To investigate the value of analyzing "weak signals" (normal deviations) alongside failures.

Main Methods:

• Review of classical failure analysis techniques and their limitations.
• Conceptual exploration of whole system modeling approaches.
• Analysis of "weak signals" as indicators of system behavior.

Main Results:

• Classical methods may not capture emergent behaviors of sociotechnical systems.
• Whole system models could offer better insights into real-world system dynamics.
• "Weak signals" provide valuable data for safety analysis beyond failures and near misses.

Conclusions:

• A return to simpler, whole system models may be necessary for robust safety assessment.
• Analyzing normal deviations ("weak signals") is crucial for proactive safety management.
• Focusing on obvious causes and subtle deviations is more effective than relying solely on intricate failure scenarios.