Answerable and Unanswerable Questions in Risk Analysis with Open ‐World Novelty

This article offers an AI/machine learning perspective o n recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open‐world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, di saster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open‐world novelty and unpredictable events in large‐scale real‐world planning, policymaking, and risk management.
Source: Risk Analysis - Category: International Medicine & Public Health Authors: Tags: Perspective Source Type: research