Defining the analytical complexity of decision problems under uncertainty based on their pivotal properties

Area of Science:

• Decision Analysis
• Risk Management
• Complexity Science

Background:

• Uncertainty presents a significant challenge in decision analysis and risk management.
• Poorly understood problems lead to high variability and bias in probabilistic estimations.
• Existing literature lacks a quantitative measure for analytical complexity.

Purpose of the Study:

• To develop a quantitative score for analytical complexity.
• To identify properties of decision problems that mitigate uncertainty.
• To build upon previous hierarchies of uncertainty.

Main Methods:

• Reviewed over 90 strategies for coping with uncertainty.
• Included methods used by expert decision-makers (engineers, military planners).
• Analyzed properties enabling problem solutions despite uncertainty.

Main Results:

• Identified pivotal properties such as small action space and reversibility.
• These properties enable problem solutions even with high uncertainty.
• Defined analytical complexity score based on the availability of these properties.

Conclusions:

• A quantitative score for analytical complexity can be defined.
• Problem properties like reversibility are key to simplifying complex decisions under uncertainty.
• This score aids in decision analysis and risk management.