Answerable and Unanswerable Questions in Risk Analysis with Open-World Novelty.

Area of Science:

• Decision analysis, risk analysis, artificial intelligence (AI), machine learning, probabilistic causal models, complex systems, open-world uncertainties.

Background:

• Traditional decision and risk analysis face limitations with unpredictable events and complex systems where probabilities are unknown.
• Open-world uncertainties regarding existence, possibilities, agent knowledge, and actions render standard analysis questions undecidable.
• Artificial intelligence (AI) techniques enable agents to learn, plan, and act effectively despite these uncertainties in various applications.

Purpose of the Study:

• To offer an AI/machine learning perspective on improving decision and risk analysis.
• To review undecidability results and present principles for intelligent agents operating under uncertainty.
• To explore how AI can extend traditional methods for handling novelty and unpredictable events.

Main Methods:

• Review of undecidability results in probabilistic causal models.
• Exploration of AI/machine learning principles and methods for intelligent agents.
• Analysis of AI's capability to enable learning, planning, and safe action under uncertainty.

Main Results:

• AI techniques allow agents to learn and adapt, achieving goals despite open-world uncertainties and unpredictable events.
• Intelligent agents can adjust plans dynamically and operate safely and efficiently in complex environments.
• AI provides a framework to extend decision and risk analysis beyond traditional limitations.

Conclusions:

• AI and machine learning offer powerful tools to overcome limitations in decision and risk analysis for complex, uncertain systems.
• These principles can enhance planning and policy-making in diverse fields like business, public policy, and disaster management.
• The integration of AI extends traditional risk management to better address novelty and unpredictable events in real-world applications.