Operational Risk Management is Ineffective at Addressing Nonlinear Problems

Abstract

A good indication of how much damage will result from a fire is the number of firemen fighting it. The more firemen fighting a fire, the more damage occurs. Therefore, in order to reduce the resulting damage, fewer firemen should be used to fight fires. This example illustrates how the wrong conclusion can be arrived at by taking a complex, nonlinear problem and oversimplifying it to fit a linear solution. Plotting the number of firemen versus the degree of damage will yield a linear graph from which one can draw the conclusion that more firemen will result in more damage. In linear systems, each variable's assigned value is independent of any other variable in the system. Keeping the rest of the system constant, one can manipulate only one variable and the system will give perfectly predictable results as the value of the one variable moves along its entire range. A matrix is another way of representing every permutation of possible outcomes of a linear system. Because the Marine Corps risk management process, called operational risk management (ORM), is a linear solution system, it cannot be effectively used to address more complex, nonlinear problems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 20, 2009
Accession Number
ADA517847

Entities

People

  • M. F. Rubinstein

Organizations

  • Marine Corps Combat Development Command

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accidents
  • Addressing
  • Business Administration
  • Hazards
  • Human Behavior
  • Linear Systems
  • Marine Corps
  • Marine Corps Personnel
  • Motorcycles
  • New York
  • Nonlinear Systems
  • Personal Protective Equipment
  • Protective Equipment
  • Risk
  • Risk Analysis
  • Risk Management
  • Statistical Inference

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Organizational Psychology.
  • Regression Analysis.