Optimal Targeting in Dynamic Systems

Abstract

Approved for Public ReleaseMachine learning based methods for optimal targeting have been rapidly adopted across several fields. A limitation of these methods, however, is that they have largely been developed for optimizing static, one-time decisions. In dynamicdecision-making problems, any action we take may induce both an immediate benefit and a system change with indirect, longer-term consequences. The goal of the proposed research is to extend the machine learning toolkit for optimal targeting to the dynamic setting, and to develop methods that enable planners to optimize decisions by rigorously trading off short- and long-terms benefits of targeted actions.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 24, 2024
Source ID
N000142412091

Entities

People

  • Stefan Wager

Organizations

  • Office of Naval Research
  • Stanford University
  • United States Navy

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Life Cycle Cost Analysis
  • Oncology

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms