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Related Experiment Videos

Optimal design, robustness, and risk aversion.

M E J Newman1, Michelle Girvan, J Doyne Farmer

  • 1Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA.

Physical Review Letters
|July 5, 2002
PubMed
Summary
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Highly optimized tolerance in engineered systems leads to power-law distributions of failures. Introducing risk aversion truncates these power laws, significantly reducing catastrophic events and enhancing system robustness.

Area of Science:

  • Complex systems analysis
  • Optimization theory
  • Statistical modeling

Background:

  • Engineered systems often exhibit high levels of optimization.
  • Such optimization can lead to power-law distributions in failure events.
  • The forest fire model serves as a key example of this phenomenon.

Purpose of the Study:

  • To provide an analytic solution for the highly optimized forest fire model.
  • To explain the underlying reasons for power-law distributions in system failures.
  • To investigate the impact of risk aversion on system robustness.

Main Methods:

  • Developed an analytic solution for the highly optimized forest fire model.
  • Generalized the model to include risk aversion parameters.

Related Experiment Videos

  • Analyzed the resulting changes in failure event distributions.

    • Main Results:

    • The analytic solution elucidates the origin of power-law distributions in failure events.
    • Incorporating risk aversion leads to a truncation of the power-law tails.
    • This truncation substantially decreases the probability of extreme, catastrophic failures.

    Conclusions:

    • Highly optimized tolerance explains power-law failure distributions in engineered systems.
    • Risk aversion is a critical factor in mitigating catastrophic events.
    • The generalized model offers enhanced robustness for engineered systems.